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    "# Introduction: Bayesian Optimization using Hyperopt\n",
    "\n",
    "In this notebook we will walk through the basics of [Bayesian Model-Based Optimization](https://sigopt.com/static/pdf/SigOpt_Bayesian_Optimization_Primer.pdf) using [Hyperopt](https://jaberg.github.io/hyperopt/) to find the minimum of a function. After we are familiar with the basic concepts, we can use these powerful methods to solve many problems, inlcuding [hyperparamter optimization](https://en.wikipedia.org/wiki/Hyperparameter_optimization) of machine learning models. "
   ]
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    "## Bayesian Model-Based Optimization Primer\n",
    "\n",
    "There are four parts to an optimization problem:\n",
    "\n",
    "1. Objective function: what we want to minimize\n",
    "2. Domain space: values of the parameters over which to minimize the objective\n",
    "3. Hyperparameter optimization function: constructs the surrogate function and chooses next values to evaluate\n",
    "4. Trials: score, parameter pairs recorded each time we evaluate the objective function\n",
    "\n",
    "Evaluating the objective function is the expensive part of optimization, so ideally we want to limit calls to this function. One way we can limit calls is by choosing the next values to try in the objective function based on the past results. \n",
    "Bayesian optimization differs from random or grid search by doing exactly this: rather than just selecting from a grid __uninformed__ by past objective function evaluations, Bayesian methods take into account the previous results to try more promising values. They work by constructing a probability model of the objective function (called a surrogate function) $p(\\text{score} | \\text{parameters}$ which is much easier to optimize than the actual objective function. \n",
    "\n",
    "After each evaluation of the objective function, the algorithm updates the probability model (usually given as $p(y | x)$ incorporating the new results. [Sequential Model-Based Optimization (SMBO) methods are a formalization of Bayesian optimization](https://towardsdatascience.com/a-conceptual-explanation-of-bayesian-model-based-hyperparameter-optimization-for-machine-learning-b8172278050f) that update the probability model sequentially: every evaluation of the objective function with a set of values updates the model with the idea that eventually the model will come to represent the true objective function. This is an application of Bayesian Reasoning. The algorithm forms an initial idea of the objective function and updates it with each new piece of evidence.\n",
    "\n",
    "The next values to try in the objective function are selected by the algorithm optmizing the probability model (surrogate function) usually with a criteria known as Expected Improvement. Finding the values that will yield the greatest expected improvement in the surrogate function is much cheaper than evaluating the objective function itself. By choosing the next values based on a model rather than randomly, the hope is that the algorithm with converge to the true best values much quicker. The overall goal is to evaluate the objective function fewer times by spending a little more time choosing the next values. Overall, Bayesian Optimization and SMBO methods:\n",
    "\n",
    "* Converge to a lower score of the objective function than random search\n",
    "* Require far less time to find the optimum of the objective function\n",
    "\n",
    "So, we get both faster optimization and a better result. These are both two desirable outcomes especially when we are working with hyperparameter tuning of machine learning models! \n",
    "\n",
    "### Sequential Model Based Optimization using the Tree Parzen Estimator\n",
    "\n",
    "SMBO methods differ in part 3, the algorithm used to construct the probability model (also called the Surrogate Function). Several options are for the surrogate function are:\n",
    "\n",
    "* Gaussian Processes\n",
    "* Tree-structured Parzen Estimator\n",
    "* Random Forest Regression\n",
    "\n",
    "Hyperopt implements the Tree-strucuted Parzen Estimator. The details of this algorithm are not that complicated, but we will not discuss them in this notebook (for details refer to [this article.]) We don't need to worry about implementing the algorithm because Hyperopt takes care of that for us. We just have to make sure we have properly defined the __objective function__ and the __domain of values to search over__. Then, we select the algorithm and let it run. Hyperopt will automatically keep track of past results to inform the model (part 4.) but we can also use a custom object to get more information about the optimization procedure. \n",
    "\n",
    "## Hyperopt\n",
    "\n",
    "Hyperopt is an open-source Python library for Bayesian optimization that implements SMBO using the Tree-structured Parzen Estimator. There are a number of libraries available for Bayesian optimization and Hyperopt differs in that it is the only one to currently offer the Tree Parzen Estimator. Other libraries use a Gaussian Process or a Random Forest regression for the surrogate function (probability model). \n",
    "\n",
    "In this notebook, we will implement both random search (Hyperopt has a method for this) as well as the Tree Parzen Estimator, a Sequential Model-Based Optimization method. We will use a simple problem that will allow us to learn the basics as well as a number of techniques that will be very helpful when we get to more complex use cases. With all that out of the way, let's get started with Hyperopt! \n",
    "\n",
    "#### Resources\n",
    "\n",
    "For more details on Bayesian Model Based Optimization, here are some good articles:\n",
    "\n",
    "* Algorithms for Hyper-Parameter Optimization [Link](https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf)\n",
    "* Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures [Link](http://proceedings.mlr.press/v28/bergstra13.pdf)\n",
    "* Bayesian Optimization Primer [Link](https://sigopt.com/static/pdf/SigOpt_Bayesian_Optimization_Primer.pdf)\n",
    "* Taking the Human Out of the Loop: A Review of Bayesian Optimization [Link](https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf)"
   ]
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   "source": [
    "# Good old pandas and numpy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Unfortunately I'm still using matplotlib for graphs\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
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   "source": [
    "# Objective\n",
    "\n",
    "\n",
    "For our objective function, we will use a simple polynomial function with the goal being to find the minimu value. This function has one global minimum over the range we define it as well as one local minimum.\n",
    "\n",
    "When we define the objective function, we must make sure it returns a single real-value number to _minimize_. If we use a metric such as accuracy, then we would have to return the _negative_ of accuracy to tell our model to find a better accuracy! We can also return a dictionary (we will see this later) where one of the keys must be \"loss\". Here we will just return the output of the function $f(x)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "def objective(x):\n",
    "    \"\"\"Objective function to minimize\"\"\"\n",
    "    \n",
    "    # Create the polynomial object\n",
    "    f = np.poly1d([1, -2, -28, 28, 12, -26, 100])\n",
    "\n",
    "    # Return the value of the polynomial\n",
    "    return f(x) * 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum of -219.8012 occurs at 4.8779\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18535b2e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Space over which to evluate the function is -5 to 6\n",
    "x = np.linspace(-5, 6, 10000)\n",
    "y = objective(x)\n",
    "\n",
    "miny = min(y)\n",
    "minx = x[np.argmin(y)]\n",
    "\n",
    "# Visualize the function\n",
    "plt.figure(figsize = (8, 6))\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.title('Objective Function'); plt.xlabel('x'); plt.ylabel('f(x)')\n",
    "plt.vlines(minx, min(y)- 50, max(y), linestyles = '--', colors = 'r')\n",
    "plt.plot(x, y);\n",
    "\n",
    "# Print out the minimum of the function and value\n",
    "print('Minimum of %0.4f occurs at %0.4f' % (miny, minx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Domain\n",
    "\n",
    "The domain is the values of x over which we evaluate the function. First we can use a uniform distribution over the space our function is defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "from hyperopt import hp\n",
    "\n",
    "# Create the domain space\n",
    "space = hp.uniform('x', -5, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "We can draw samples from the space using a Hyperopt utility. This is useful for visualizing a distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x185362a0b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from hyperopt.pyll.stochastic import sample\n",
    "\n",
    "\n",
    "samples = []\n",
    "\n",
    "# Sample 10000 values from the range\n",
    "for _ in range(10000):\n",
    "    samples.append(sample(space))\n",
    "    \n",
    "\n",
    "# Histogram of the values\n",
    "plt.hist(samples, bins = 20, edgecolor = 'black'); \n",
    "plt.xlabel('x'); plt.ylabel('Frequency'); plt.title('Domain Space');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "When running, our algorithm will sample values from this distribution, initially at random as it explores the domain space, but then over time, it will \"focus\" on the most promising values. Therefore, the algorithm should more values around 4.9, the minimum of the function. We can compare this to random search which should try values evenly from the entire distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Hyperparameter Optimization Algorithm\n",
    "\n",
    "There are two choices for a hyperparameter optimization algorithm in Hyperopt: random and Tree Parzen Estimator. We can use both and compare the results. Using the `suggest` algorithm in these families automatically configures the algorithm for us. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "from hyperopt import rand, tpe\n",
    "\n",
    "# Create the algorithms\n",
    "tpe_algo = tpe.suggest\n",
    "rand_algo = rand.suggest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# History\n",
    "\n",
    "Storing the history is as simple as making a `Trials` object that we pass into the function call. This is not strictly necessary, but it gives us information that we can use to understand what the algorithm is doing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "from hyperopt import Trials\n",
    "\n",
    "# Create two trials objects\n",
    "tpe_trials = Trials()\n",
    "rand_trials = Trials()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Run the Optimization\n",
    "\n",
    "Now that all four parts are in place, we are ready to minimize! Let's do 2000 runs of the minimization with both the random algorithm and the Tree Parzen Estimator algorithm. \n",
    "\n",
    "The `fmin` function takes in exactly the four parts specified above as well as the maximum number of evaluations to run. We will also set a `rstate` for reproducible results across multiple runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x': 4.878481851906148}\n"
     ]
    }
   ],
   "source": [
    "from hyperopt import fmin\n",
    "\n",
    "# Run 2000 evals with the tpe algorithm\n",
    "tpe_best = fmin(fn=objective, space=space, algo=tpe_algo, trials=tpe_trials, \n",
    "                max_evals=2000, rstate= np.random.RandomState(50))\n",
    "\n",
    "print(tpe_best)\n",
    "\n",
    "# Run 2000 evals with the random algorithm\n",
    "rand_best = fmin(fn=objective, space=space, algo=rand_algo, trials=rand_trials, \n",
    "                 max_evals=2000, rstate= np.random.RandomState(50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum loss attained with TPE:    -219.8012\n",
      "Minimum loss attained with random: -219.8012\n",
      "Actual minimum of f(x):            -219.8012\n",
      "\n",
      "Number of trials needed to attain minimum with TPE:    655\n",
      "Number of trials needed to attain minimum with random: 235\n",
      "\n",
      "Best value of x from TPE:    4.8785\n",
      "Best value of x from random: 4.8776\n",
      "Actual best value of x:      4.8779\n"
     ]
    }
   ],
   "source": [
    "# Print out information about losses\n",
    "print('Minimum loss attained with TPE:    {:.4f}'.format(tpe_trials.best_trial['result']['loss']))\n",
    "print('Minimum loss attained with random: {:.4f}'.format(rand_trials.best_trial['result']['loss']))\n",
    "print('Actual minimum of f(x):            {:.4f}'.format(miny))\n",
    "\n",
    "# Print out information about number of trials\n",
    "print('\\nNumber of trials needed to attain minimum with TPE:    {}'.format(tpe_trials.best_trial['misc']['idxs']['x'][0]))\n",
    "print('Number of trials needed to attain minimum with random: {}'.format(rand_trials.best_trial['misc']['idxs']['x'][0]))\n",
    "\n",
    "# Print out information about value of x\n",
    "print('\\nBest value of x from TPE:    {:.4f}'.format(tpe_best['x']))\n",
    "print('Best value of x from random: {:.4f}'.format(rand_best['x']))\n",
    "print('Actual best value of x:      {:.4f}'.format(minx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "The Tree Parzen estimator and random search found the exact same minimum of the function to 4 decimal places. Sometimes even random search gets lucky (especially when we run it for so many iterations. However, we can see that TPE found the minimum in about half the number of iterations. After finding this minimum, we could have stopped running the algorith!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Let's see if there is any difference in time between the two methods. We can use the built in Jupyter magic command for timing (running the function 3 times)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "436 ms ± 20.1 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 3\n",
    "# Run 2000 evals with the tpe algorithm\n",
    "best = fmin(fn=objective, space=space, algo=tpe_algo, max_evals=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90.7 ms ± 2.68 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 3\n",
    "\n",
    "# Run 2000 evals with the random algorithm\n",
    "best = fmin(fn=objective, space=space, algo=rand_algo, max_evals=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "As a point of interest, the random algorithm ran about 5 times faster than the tpe algorithm. This shows that the TPE method is taking more time to propose the next set of parameters while the random method is just choosing from the space well, randomly. The extra time to choose the next parmaeters is made up for by choosing better parameters that should let us make fewer overall calls to the objective function (which is the most expensive part of optimization). Here we ran the same number of totals calls, but this was probably not necessary because the Tree Parzen Estimator will converge on the optimum quickly without the need for more iterations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Results\n",
    "\n",
    "We see that both models returned values very close to the optimal. To see how they differ in the search procedure, we can take a look at the trials objects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>36.210073</td>\n",
       "      <td>0</td>\n",
       "      <td>5.957885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-202.384052</td>\n",
       "      <td>1</td>\n",
       "      <td>4.470885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-75.519449</td>\n",
       "      <td>2</td>\n",
       "      <td>3.218963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.543552</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.515859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.078011</td>\n",
       "      <td>4</td>\n",
       "      <td>-4.916832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss  iteration         x\n",
       "0   36.210073          0  5.957885\n",
       "1 -202.384052          1  4.470885\n",
       "2  -75.519449          2  3.218963\n",
       "3    5.543552          3 -0.515859\n",
       "4   35.078011          4 -4.916832"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpe_results = pd.DataFrame({'loss': [x['loss'] for x in tpe_trials.results], 'iteration': tpe_trials.idxs_vals[0]['x'],\n",
    "                            'x': tpe_trials.idxs_vals[1]['x']})\n",
    "                            \n",
    "tpe_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Extracting these results was a little work. We could have formatted the objective function to return more useful information (in a little bit we'll how to do this)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "First we can plot the values that were evaluated over time. As the algorithm progresses, these should tend to cluster around the actual best value (near 4.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>loss</th>\n",
       "      <th>iteration</th>\n",
       "      <th>x</th>\n",
       "      <th>rolling_average_x</th>\n",
       "      <th>rolling_average_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>655</td>\n",
       "      <td>-219.801188</td>\n",
       "      <td>655</td>\n",
       "      <td>4.878482</td>\n",
       "      <td>4.876424</td>\n",
       "      <td>-219.782758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1369</td>\n",
       "      <td>-219.801173</td>\n",
       "      <td>1369</td>\n",
       "      <td>4.877646</td>\n",
       "      <td>4.876424</td>\n",
       "      <td>-219.782758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>161</td>\n",
       "      <td>-219.800919</td>\n",
       "      <td>161</td>\n",
       "      <td>4.879614</td>\n",
       "      <td>4.876424</td>\n",
       "      <td>-219.782758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>230</td>\n",
       "      <td>-219.800612</td>\n",
       "      <td>230</td>\n",
       "      <td>4.875995</td>\n",
       "      <td>4.876424</td>\n",
       "      <td>-219.782758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>969</td>\n",
       "      <td>-219.800430</td>\n",
       "      <td>969</td>\n",
       "      <td>4.880573</td>\n",
       "      <td>4.876424</td>\n",
       "      <td>-219.782758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   level_0  index        loss  iteration         x  rolling_average_x  \\\n",
       "0        0    655 -219.801188        655  4.878482           4.876424   \n",
       "1        1   1369 -219.801173       1369  4.877646           4.876424   \n",
       "2        2    161 -219.800919        161  4.879614           4.876424   \n",
       "3        3    230 -219.800612        230  4.875995           4.876424   \n",
       "4        4    969 -219.800430        969  4.880573           4.876424   \n",
       "\n",
       "   rolling_average_loss  \n",
       "0           -219.782758  \n",
       "1           -219.782758  \n",
       "2           -219.782758  \n",
       "3           -219.782758  \n",
       "4           -219.782758  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpe_results['rolling_average_x'] = tpe_results['x'].rolling(50).mean().fillna(method = 'bfill')\n",
    "tpe_results['rolling_average_loss'] = tpe_results['loss'].rolling(50).mean().fillna(method = 'bfill')\n",
    "tpe_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18536fc7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 8))\n",
    "plt.plot(tpe_results['iteration'], tpe_results['x'],  'bo', alpha = 0.5);\n",
    "plt.xlabel('Iteration', size = 22); plt.ylabel('x value', size = 22); plt.title('TPE Sequence of Values', size = 24);\n",
    "plt.hlines(minx, 0, 2000, linestyles = '--', colors = 'r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "We can see that over time, the algorithm tended to try values closer to 4.9. The local minimum around -4 likely threw off the algorithm initially, but the points tend to cluster around the actual minimum as the algorithm progresses. \n",
    "\n",
    "We can also plot the histogram to see the distribution of values tried."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18537063438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.hist(tpe_results['x'], bins = 50, edgecolor = 'k');\n",
    "plt.title('Histogram of TPE Values'); plt.xlabel('Value of x'); plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Sure enough, the algorithm tried many values closer to 4.9 that anywhere else! This clearly shows the benefits of choosing the next values based on the past values: more evaluations of promising values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Loss of -219.8012 occured at iteration 655\n"
     ]
    },
    {
     "data": {
      "image/png": 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nsHPnTr9y2Ww2vve97/Gb3/yG+fPne8PqN23ahJSSKVOmsGPHDr/fzJ8/n//93//ll7/8JUVFRXzxi19k/PjxdHR0cOTIETZv3kxOTg7vvfdev+o5sFzPPfccS5cu5Xvf+x7PP/88s2bNwu12c+DAAd59910++OAD71rZ008/zeWXX84dd9zBX/7yF2bOnElSUhLHjh1jz549bN++nb///e9kZ2f3mm9sbCyPP/44y5Yt4ytf+QpLlixh/Pjx7NixgzVr1pCYmMgTTzwR9JCGgfI///M/fltPfLnzzjspKCjgiSee4Ctf+Yp3f6Kxj/H1118nIiKC5cuXe6M/jx07xhe/+EXy8/OZPn06WVlZtLa2sm7dOg4cOMBll11GXl4eAI8++ijr1q1j7ty5jB8/nvj4ePbv389//vMfoqOj/Q4quPbaa9m2bRtPPvkk06dP5wtf+AI5OTk0NjZy+PBhNm/ezEUXXcQLL7zQ77RNesdUjCajjh07dvRQDr7cfPPNfSrGxx57jLfffptNmzZRXFxMdXW19xixyy67jP/+7//226ANympcsWIFr7zyCs8//zxvv/22N7AhOzubO++8s8cazW233UZ6ejp/+tOfePHFF4mLi+MLX/gC9913HzfeeGPQsv3whz8kOjqaZ599lueee47k5GQWLVrE//zP/3i3UgTyve99j8997nMsX76cLVu28NZbbxEXF0dGRgZXXnmlN7hoMEyfPp1Nmzbxxz/+kTVr1rB8+XKio6PJycnhtttu8wsyysjIYP369Tz11FO89tprvPLKK7hcLlJTU8nLy+N3v/sdc+fODSvfhQsXsmbNGh5++GE2bNjAa6+9hsPh4Oqrr+ZHP/qR31aYoSDwJBpfrrnmGgoKCjjvvPN49913eeihh3j33Xd55513SExMZNGiRdx1111++zNzcnL46U9/6u1rtbW1JCYmctZZZ3HHHXf4bfm48cYbSUpKoqSkhA8++ACXy0VGRgZXXXUVt912W48I0gcffJAFCxbwzDPP8N5779HQ0EBiYiKZmZl861vf4sorrxxw2iahEU6ns6cfxMTEZNAsWrSI4uJi8zYHE5NTDHON0cTExMTExAdTMZqYmJiYmPhgKkYTExMTExMfzDVGExMTExMTH0yL0cTExMTExAdTMZqYmJiYmPhgKkYTExMTExMfTMU4zPheU2QyNJh1OjyY9Tr0mHU6PAx3vZqK0cTExMTExAdTMZqYmJiYmPhgKkYTExMTExMfTMVoYmJiYmLig6kYTUxMTExMfDCvnRpFVFQISkst1NQIHA5JYaGHrKwz+2Aio0727dNobAS7HWJj7cTEiFFXN2b7ndoMd/udqumfif3aVIyjhIoKwerVNuLjdVJTJS0tsHq1jUsvdY3Y4BnpAWHUicslOXRIQ9MkjY2ClBSbt26AQZdxKN6zt/YbijL2pxzDndepKuBDUVKi8fzzVkpLrTgcOnPmeIiJ6Tn+QpUrnPIG6x8vvGAjNVWi6wz6PfsrP3ors+93mgbV1YKcnOGRSyMtY0Jhueeee+4d6UKcztTX15OSktLncxs3WhFCkpAAQkBkJICkrk6joEAHVCfauNFKcbGFigqN2Fj1fLgYg0cISVIStLbCzp1W0tL0Hun059nhwqgTpRQhMVF93tTURW5uJPv2Wdi71zKoMvb3PUO1Qaj2Cyzj0aOC116zsXu3Rl2d6Hcbhvsuui74+GMLK1facDqV0OnrfQ4ccJGeHtdreQbSLyoqBK++amPFChtvvWWlqkrrtTwD6XcDHRslJRqPPhrJ8eMaiYngdgt27bKQlaWTnNw9/kKVS0rJpk2hy2uM/8D+0dIi2L7dQnMzTJ4s/X7X3Nz/dwlHfoRTx83N/t+VlGgcP66RlSWJje093XDbprmZE33TyqpVNiIjJZmZsl/jN1y5OlBMi3GUUFMjSE31nynFxanZGgyNRVlaaiE+vrvTqX91SkstZGW5+/3scM/2jDppahLY7eqz6GiorrYQFwdbt2qcf74nrPcJVdb+1ElvbRCq/XzLWFcn2LvXgqZJmpqUQBrs7Nv3vQ4eFGRkSFwu9Vl0tCQ1VWf3bkF1dU/rBPB7n/37RZ/l6au+Aus5I0Nn3Tor5eUadrtK8/331fdXX+3qYXEZ7xCY/tq1VlJSpF/7GeXZt0/j0CGNSZM8ZGf3b2y8/roVu12npcVCVJQkOhpA8sEHFq66yu0df6He+/XXrUye3Hf/CewfRn10dSmrzPc9XS7R73Hel/wI1YZ1dYLyco2qKsGRIzaSkqCqStDVZSUxUeJ0CpKSJOXlGikpnl7TDUbgmDlyRPDKK5HMmuWmqQk0TbJ3r4W4OA8pKTJo3Y0EpmIcJTgcahD4zpRaWtTn0D+lBsEVQX8GT7BnOzsFW7dqYblYBqM0jd9u364RFQWaBu3tEBOj/o2L89DSAlKq8vf1PqHcWDYbrFljJSpKkpsrOfdcNTjDESjg3wah2s+3jOXlGtHRSvg6naLPNgynnnzf66OPNBobNWw2iI6WxMSo/Csq1PpsXZ3OvHm6t61sNun3PnFxOnFxvZentz4UrJ5XrIhA1yEpSZUHQAhJba2qT/CwerUNt1tSVaWxZYuF2Fi4+GIXeXkqn85OZXEsWODyaz8hIDtbH5SAPXZMIztbUlcn6epSFlFcHNTWan7jL9R7HzumMWuW3uPzwP4T2D8aGwUREZKEBOn3u/5M9npLH/zlhy/Gu9TVCUpK1AQqLU0pvw0bNKZOVe3X3q6UpM0GXV19pxuMwDFTXa1ht+tUVwuamwXJydDe3q14+6N0hxMzKnWUUFjooblZo6kJdB2amqC5WfPOjGtqRFAlUFPTsxMZAqq1FVJTpdcyaWwUvPeehbVrrXz4oYW6OhGykxsDzaCuTrBli4WICElqqmT3bjXTdLm6Z7xut+TJJ208/HAEDz4YydGjagAePSp48MFIHn44gpUrrVRUhO74vmUvLNRxOgVVVYLqao26OmhrE6SkuGhu1pg+XfcrIwQftL6DU9PA5RJ8+qnG2rUW4uMlQsDevRobN/ZeJ721QWD7HTok2LzZSksLvPeeSrexURAdrZS7IRBDtWFfVFQInnzSRmmpxt69FhoaVF0LIfnsM3HC8lF5tbYK7Hbp11bx8TrbtmlB36esTGPlSivPPmvr0V6B/QLgyBFl6f3+9xEcOCB69Iljx7rLA8rqd7kENTVqAuR2K6XW2Qnp6RK3W7J+vY26OpXv7t0aDofH237KRSmorVX/bwjY6GglYPtTr5mZOg0NkJ6u09kJnZ3Q3Ayxsbrf+Av23i0t6vd99cGKCkFdnWDNGhsbN6rJYkSEssZyc6Xf70JN9vp6l77khy/GuxgTtZgY6OgAj0dNYGpqBEKoiejYsToHD6ry9pZuRYUI2mcCx0xjoyApCZqaBAkJSvlGR6vPg9XdSGEqxlFCVpbk0ktdxMaqGVNsLH7uk1ADM1gnClQEhoDasUPD6VSdvL0diostHD4cfPAEDrSdO1VXmTJFepWL3d4tiOrqBHv2WKitFX4z+PLy4O7DUMrRt+wOh2TOHA+ZmTqappOQIBk3TictzcWll7q45BJ3WMIgcHCWl2t0dQksFsG4cWq2HxEhaWxU79mXQAnWBr7tt2+fxu7dFiZO9DBvnlLuxcUWhID6emhv7xaIvQmCUMLGmDzU1grS0iQdHVBSYiE5GXRd0NkJbW3qv/Z2QUyMSj/QOpFS5V9XJ/jwQwtbtiTw1ltWduzQekyqjLyDTQC2brWSnq76BaiyGErN4VCWWHt793u1t4PNpp5ft87CmjVWjh8XeDyC9HSdiAjo6JAcOKDyqa7WmDy5p/fC5RLe9xqogF2yxI3TqeFywVln6bS3K6V7wQW63/gLpXiWLOm9D1ZVqUCxqCjJhRe6kVKyYYOV6GiJywVbtlj44AMLhw6Jfk32AulLfvhivEtVlSAqyr+f5OXpNDYK2tq6lXRcHEycqIdMN9REvKJC9BgziYmShgbVZrm5kvZ2QX29+rs3ZX6yOa1cqffffz8PPPCA32epqans27cPACklv/vd73juuedwOp0UFRXx+9//nkmTJo1EcXuQlSVDuksKC5XLCXTi4tRgaW7WmDvX1ePZYG6fqiqNyEjJ7Nk6Bw4ImpoEiYnKZRLKxWmzSd5/3+INFpg713BTqY7c0SG8gqi8XEMI6V0TNFwkH3xgITvb333Y0KAsy/HjZQ83a2DZU1Ik8+ZJqqsFN9yg3rWszElWlgNQg7S01EJ1tRqEeXluSkstrFnT7cIN5sZqa1OKPSEBcnN1jh/XqK3V6Ojw9CpQemsDo/1WrrSSmdntPpozx8OOHYL6eo3oaMHEiR6SkroFQbA27G0905g8pKZqdHSIEy5KSUODStti0aiuVlZWYaHO7t1qQjRnTrfLr6UFpk/XOXxY86532WzK2kxONqxL2cOVZwhgo84rKwWzZrkZP15SXS3p7BReyy0lxUNamiQtDRoaBFKqOnU6BWPGSK/l5DkhB/fv18jL009YcYKjRwVFRTB/vpvISP/2iIyU3vRycyUlJRrt7RK7vfd6DaSoSOe22zp5/XUrx45pTJvmYckSN0VF/u7RwPd2OCRz56p+kp7e/bmmqXHz0ks2nE7YuzeTCRME55yjfnPhhZJDhyS7d1soKvKc8IYI6ustLFvWRXq6DHucB9Kb/DAwlimamsDpVB6Y8eN1Jk9WcsHpFEyZ4iEyUlm0Nptk4UI3110XOv/e1ixnz/awebMNt1spzchIHafTSkGBm6QkSUGBhz17LCQk6MTG4q3Tkea0UowA+fn5rFy50vu3xWLx/v8f//hHHnvsMR577DHy8/N58MEH+epXv8qHH35IfHz8SBQ3bHobmIEEW29Qv9FJSZFe5abrwf35vkL5kkvUet7mzVba2rqfyc2VFBcrgarrai3CalVK5sABNQONjob6eo2CAo/XfWhYli6XZNYs3U/gZ2XJfq2VGPXiGwxklNtiURbxq6/amDrVg8sFOTlK2EREKKGamKiEX0ICWK06mZmSuXP1XsPbm5rgyBENux3y8/WgbRBauUsWLHCH1Ya9rWca6RsKASRRUaoNMjMFd93V5U2jpkYwcaJOTY0ScrreLWwvvdTF2rVW6up0uroEERH6iWhMeg228K3zZ5+1ed/VKE9UlBKqTU1gswluvrmTHTssbNumISV87nMq3agoSXq6YO9ecLlUuxw6JMjIUJOJ7GzJ4sVub7v6KovkZOUCb2pi0AK2qEinqKirz+dCKR7jc6OcbreKpBZCcvx4BA6HWssrKlITy6oqDbdbMn68+g/Ue1RWahQVucMe5/3Fd3ycfbZOdLTkgw+spKVJkpIkqalw8KCF8893e4OYmps1Lrmkd2Ubas3ys880Dh+2UFDgobNT9SGr1cJ//VcXbreaEGRnS5Ys6RwVytCX004xWq1W0tLSenwupeSJJ57g+9//PpdffjkATzzxBPn5+fy///f/uOGGG052UftNODNCCG7ZWK2Qlubf+cJxxYISyhMneti920JSkpu4ODUrzs3VcTiUNTdmjE56erfSNWbwSUk69fUAgsmTdT/L0jcaz7BI+mMZhyp3YFTm8eMwZgx0dCgX0cSJOlarpLZWo7W124rJzZVB3TiBAsUoU6h9bJpGSOUebhv2FuRiTB5SUiRFRR4OHFDrsGPG+Lv/fPMxyhgobHUd5s3T0TQ4dqyVo0eT/DwBvmUPhu9ExijPjh3qt74KKtACe/ZZG3FxyuV+8cUe1q2z0NUlsFqVkrPZBIWF7hPv0XNSeM013ftDq6uV1XzeeR708HYQDAtG/9u710JMjFq7i4/Xqa5WkwZjsmFMUn3xnXyE20cGWj6jXyql7KayUhAZCdnZklmzOqms1PqllIOtWba1qa0vY8bodHXB+eer921qUp8vXjyyUad9cdopxoMHDzJp0iRsNhszZszgF7/4BePHj+fQoUNUVVVx8cUXe5+Njo7mggsuYOvWraeEYuwLXwFts0mvInA4JMuWdVFaaqWpSQ7IFZuTI2lv171rGA6H9IbbG3mvXm2jqUn6zeDz83Xq6zWv+9DXsjQIFAoDnTEb5S4p0fyiMp1OQXbHyj5jAAAgAElEQVS2h9hYvAOyokKwdq3Vz4q55BJ30I3ZTz5po7ZWIy1NcNZZul/UoxFV6evyrKkRSCm8Fmp/lLtBb5az7+QhKUkycaIkK0vrNaQ/lLANzCfQE9BX2QMnMjabJC9PcOmlXb22mW++eXnqPXbsEHR1KSuisNC/LUJba26/iYtR38NxOEYggROisjK1t6+xUXi3pqSnd3L0aCxSdlvR/ZmkDiWhxnVUFN5lCkX/ZhdGH6iqUmvevmuWSUn4TbJGS9RpX5xWinHGjBk8/vjj5OfnU1tby0MPPcSCBQt4//33qaqqAsDhcPj9xuFwUFlZORLFHdJ9gMHWpAx3mZGm71pIMIUTuE3inHN0rwXY0gIFBXrImV6gQvN1kfhaK4GWpZG2r1AY6IzZELa+gslw4QZzB/a2bmLUR7Agl6IipeSrq0VQ6zo7W6ejQ/hNIvrrDuvNch7M5KG3fHS9pyegr7QHWpaBKtRg9Hcr01AQbLwdPKgmZImJ0ru9yGaDc87xcGI5lNhY+jVJHUr6u0wRLkYfOHJEKcfUVOn1DjU04B2LQ5XfyUA4nc7RX8oB0tLSwvTp0/n+97/PzJkz+dKXvsSOHTsYO3as95lbbrmFqqoqXnnllZDpDMdt0VVVNjZutBMb6yYmRqetTaO11cr8+U7S0vo/QNavt9PeLoiL8w2wUAP1oouc/SpPV5fgk0/UmmthYQs2mz6osoXKZyjeO1jahw9HIoRECEF7u2DKlFasVhl2XRgYdVpeHo3LpREVpRReRIRkwoR2oqMlDQ1WkpNd3ohMUGu39fU2li6tHfT77NoVS0ODlaQkN5Mntw66jkYyn+HK91//GjNsbRCKYOOtosJGeXkMubntlJdHI4SOlIIJE9qxWoVfHx+JOh/OsRcs/ePHI9i2LY5p05rJyHANeX6DIT8/v9fvT2vFCLB48WIKCgq4/fbbmT59OuvWreO8887zfv/1r3+d5ORkli9fPiz5l5WVBW2ElSuttLb6z96amvBz9/UHIwgiUDj4RnP2RmB56uqE17V18cWeIT3VZrCWcqg6NdJeu9bKxo1WHA4PkydLIiNlD+s5HIw6bWjoDiowglwKC3WvpTSU7RgOw3XiUG/1OpoZ6rEUDqHG2969yp1aVqbhdILbXcvs2ckjegZo4NmnRllPxjm3GRk6lZXaKddXTytXaiAdHR2UlZUxb948xo0bR1paGuvXr/cqxo6ODrZs2cKvfvWrk162/pxCEw6DdZP0tk1iqIXLcAUXGGlfd53a42gM0IGGgYcX5DLwYKFQ9HXA88k4bP5UYjABWwMl1HgLXG4oK6siP/8kHS4chHCWWIaS4GN7BCOiBshpdYj4z3/+cyIiItB1nf3793P33XdTXl7OI488gt1ux+Px8Mgjj5CXl4fH4+FnP/sZVVVV/OEPfyBSnY475IQ67LaiQm2i9s22uVldqxTu4by+xMZKdu60AhKbTaXV3Kwxb547rMOUh7o8w0k4BwgnJKhyFxbqFBQM7OBz3zpNTFRrlcnJgquu6hYqCQmQlqZTV6dmxXY7zJvXM4gnXPo6RLs/h0X3l+E+mHm4GOo2CIdwx9tI1+lw9peRxDxEvB8cO3aMG2+8kbq6OsaMGcOMGTN4++23ycnJAeCOO+6gvb2du+++27vB/1//+teI7GEc6lnuYAMyRmLWHYzRdA1NuHU6lBZwX4EkQ+1pOF0YTi9EqPyGa7/hUGL2l4Fx2q8xjjR9rYeNFiUwGsoTLOw+mNvnVF0LC4fe1ooXLHD7bB2R3q0jQ7WedjrX60gx0nU6EuuvJwNzjfE05mTPcvtipMszEmH3o41Qa1eaptYS09MlTic0NMBHH6nzWK1WcdIte5NTg9HiCTrVMBXjMGFYXzt3juGcc6wjbg2eCoyk22ekrWWDUILM94qo+HgVCFRdDZWVgu98Z/S58ExGB6eKy3e0YSrGYcD3zMRjxyI4fNjK229bWbasq8fRWCbdDNcG5L4YTZGeoQTZmjVW7w0hxnm3hovVFHImvTHSnqBTEVMxDgO+d8y5XOpG8vp6dWlrevroOzB3tDBSbp/R5sINJshGatJgYnImYirGYaCmRlBVpU6dUSewQHIyHD8u/YRtOO670eLiOxmMlNvnVIjcM9eKTExOHuZFxcOAcc6k763l7e3qc+Mm7t4u9zQI55nTjawsddXQDTe4WLx4ePeiGfTnEuiRoj8X0ZqYmAwO02IcBgoLPbz9tvXEdUvdN2RnZ+teYRuO+260ufhOV04Va8xcKzIxOTmYFuMwkJWlrnnSdUFtrZWICN875tR9fzU1whtMYRAXh9eiDPcZk8FjWmMmJia+mBbjMFFUpJOe3smbbzZhs8WdWB/sdg2GE0wxklGaZ8q6poFpjZmYmBiYFuMwkpWlrjkKtl5WWOihuVmjqUmdbNLU1H0rfH+eGWrOxHVNExMTE19MxThChOO+GwkXn++6pqZxYkO5cVu9iYmJyemP6UodQcJx351sF9+psHXBxMTEZDgxLUYTP06FrQsmJiYmw4mpGE38GIl1TRMTE5PRhKkYTfwwty6YmJic6ZhrjCY9MLcumJiYnMmYitFkxDkT902amJiMXkxXqsmIYu6bNDExGW2YFuMpzOlgaZnnwZqYmIw2TIvxFOV0sbTM82BNTExGG6ZiPEU5XU6oMfdNmpiYjDZMxXiKcrpYWua+SRMTk9GGqRhPUU4XS8vcN2liYjLaMINvTlFOlct1w8HcN2liYjKaMBXjMFJSovHcc1m0t0eRmakze7aHqiqNbds0pITp03WmTPFQWamFjCw1Ik/37dNobAS7HZKT1fdNTXDkiIbdDvn5OnPnBre0eoteLSnReP11K8eOaWRm6ixZ4qaoSO/Xe4ZKfzBRs339djgicn3T1E74UnSdIS/7ycIoR1mZhtMJiYlQUKCPePRyf+pnJPrBUJZ/NKU9lAxFOUfzuwqn0zk6SnKaUVKi8eCDkVRXt9HeHkd9PXR0CLKydCZN0jl2TGP/fo32doiL00lNhYwMnbFjJTk5kpISjb17NdraBJmZHhwOQWqqzuHDGgcPCjo6NJKSdDIzdTIyYNmyLj+F5qtQDx3SiIvTOXRIo7paQ9Mks2d7aGyEjRttREdLIiOVErDZ4M47O1m4MLw1vpISjRUrInC7IToaOjuhrU0wdaoHlwvi4yVVVRqffSZwOgXTp3uYMaN34WxE3Lrd6rfV1QKrtfsd33vvIHv2FBAfr6zlI0cEu3dbGDdOJyVFpdlfhWbkGR+v09kp2LLFQmurqvP2dv/8A9/fd2Ixe7aHo0ct3rIZlnxhobvXCdBQ89ZbFpYvj6ChQeB2w4QJOikpMHGiB5tN9HBXV1QI3nyzGpstc9jKV1Ki8fzzVkpLrTgcOtOm6bS2CqqrNebPd3PJJe4eZTLaJLCdCwp0MjJ0SkutPep6OF3xgWVqaYHDhzVSUyX19aLHBKStbR/5+flhpbt2rZWNG62kpioZERkph/19Asvgq6gyMvSgfdZ3zKemStLSdKzWnn2qr7wC67E/71pWVhZWvQ4Uyz333HPvsKV+BvP730dw4IBGQ4Ogs9OK2y1ob4eGBsHhw0rYezzQ0QFut8DtVkEz27ZZ2bNHUFOj0d4ukBIaGjSOHRMcPSrYtUsJbCGgpUUJlc5OSVmZhRkzPCQkdHc6IZTVVlEhKC62ERGhFN/+/RoffWTlwAELQkBrq4amgRACmw22bbMwb57Hu7cwFBUVgscfj0TTJJGR4oRVqwbRJ59o7N1rYccOCw0NgqYmZYLV1CghUl5uIS1ND5rHxo1WnE7Yu1eVz2IRHDokeOstGx4P7NnjJjU1hoQEqK8XfPqpBSEkLS2Czz7TOHpUIydHouuwc6c1ZD6BeQohSUiAXbssdHYKKitVG5x1lqSjAz75xMq0ad31UlKi8eijkQgBaWnQ2Ch44w0baWkexo0DISAyEhoa4LXXbNTXC44dU225f7/G+PF9l2sglJRo/OY3UWgaSCno6oLaWg27XdLRIWhsFGzcaMHtFsTGSpqbVX9pa2shOzuO1tbw660/ZXr00UiOH9dITFR9d+tWG4mJktRUSU0N1Nf79wnfNvFtZyEgMVGycqWNlBSdtLTuugZJXZ1GQUH/vB69UVEh2LjRSnGxhXfesRAbK715trQItm+3UFkpaG7W6OoCp1MjIkL18ehoJzk59j7TX73axr59KqBOCOUJSkuTREcP/fv0VgYhJElJahLy6qsRJCRIMjOlt09IKVmxIgJNkzgcSn4dP66RnKzT1ibCLqdv2w6k7err60lJSRncS/fCGRt88/TTTzNt2jTS0tK48MIL2bx585Cmv2eP2jbhdgsiIpSQ9njA7QbXiWXAtjaQUlk37e1QWamsucpK7YTSFCcUqhJmVVUaVqtKo6lJpRcTI6mt1Th8WGPtWuUZ993K0dysFLHHIzl4ULBrl4bVqlyFLS3KupMSnE5l0dXWqn/D2fZRWmrB7ZYkJ0NVFcTFSSwW2LZNY+dOC1VVgs5OpTCcTkF0tEDXlTLvbWtJTY161+hoiccjKC/XiIiAiAjYvVvw0UcJdHaqiUR5uXouORkOHtRISpIkJUkOHtT6tYXFN8rXKG9cnETX1SQkORncbklpqYWKCsHKlVZ+/etImpogIkLVZ0qK2jqzY4d/fvv3Wzh8WEMISEpSgr28vLu9hprXX7d6y+NyCeLjITJSUlamecumaXj3vq5da1hd+rBt/Xn9dSt2u7IsoqJUuWJilAckJsYop3+evm3i287NzYKEBLweBV+GOjI7cL9wba1gzx4L+/cLPvzQwquvWmloEBw8aCEmRpKSArGxkupq9T67dsX2mYcxXlWdQEwMREdLysu1kxZpHrj9q7paYLfrJzxM3X3i9det3jHf0iKoqNAoL9d47z3lsg+X0R5Vf0Yqxn/961/cc8893HXXXWzcuJFZs2Zx5ZVXcuTIkSHLIzJSepWOxaKUn5rBK8XY2akUm5Rgtap/W1qUIu3oUM9aLGpG1tysnnW5lMVnpGn83dkpSE6WbNtmWGXdna6tTSmMtjZBU5Nyq3V0qO+EgK4ula/LBVFRktZW9Uw4ndxwsbS3Q3u7wOMR1NUp5RofL3G7BU6nRmenICpKUlurZvqNjaLXQeBwKMESHQ3HjwsiI5UysdslLpcgKcnF7t2qfI2N6rn2dvVeR49q7NtnYft2jbq63vMJzNOI8jXKCEpAgUrf4VDKxRCUnZ3Ca4E3N6vnx4yRVFf7192+fap9YmJUncfEqHcx2muoOXZMY8wYSVcXRESoftjeLjh4UKOjA1pbBQkJ0ivstm3Thl1IHTumkZSk6rOrC7q6xAkXmvKkJCTIHnn6tolvOyckSO/3gRdoD3VkdqDCSE2VtLXB+vU27zh1uZQCLSuzsG2bhaNHNSorVZ02NPQ9+THGa0KCGkugliUaG8VJizQPVFRNTYKkJLzjAFSfOHZMw+GQ1NQor4fLpfpyfb3qX+EeMDLao+rPSMX42GOPcc0113Dddddx9tln89BDD5GWlsZf//rXIcvjwgs9uN3KumtvV5aTy6UUpPEfqH/lib5gtXLCAlGCw+3ufs5Qosr1qv42XLEREeBw6N50jE5XVyc4dMjiVci63v1bKZWFZ1ixVqsSOkLAuHEenM6+39HhkKSlSdrbBZomqa5Ws3gQZGcr68PjUeWoqtKoqtKw2SSJibLXQVBY6MFqhfp6JcSlVBOJxESdhARJXl4b1dUahw4JqqpgyxYlkNrb1UTAZpNYrZKSEgtHjoiwBpvvfsrx43U8HuUCTUvTaWtTbZiWJnE68QrKlBQdt1tNgo4fVwIhKkonJkb67cvs6lLtE4gcQhlgWLHPPms74YmQdHaqdq2rEzQ1KSEeEyPZt08jOVn9Li6ue1Lmy1ALqcxMnYYGSE/X6ewEISTNzaru2tsFubk9+4RvmyQkSOrr8T4LkJYmvWNmuPbABiqM3FylFDo6JNHRoGnKFa1pkqoqZS22tUFVleDIEUFSUt/R1sZ4zc1VddHWpvpxRITs9/v49oOVK60DVlQJCZKGBjVJNGhpUe2YliY5elQghCQiQnkeIiPV+nW4XobRvn/5jFOMXV1dfPLJJ1x88cV+n1988cVs3bp1yPK58koX8+a50DQl4IVQCiwqSv2/24038tHlUsLJcLNlZek0N3dbhtYTk86ICPVZVBRe12xnp2DyZBcej2D6dCV8MzJ0Nm+28s9/WnE6VcCP263Sstm6lfHYsZLYWNXx3W6V97nnuhgzRpCY6P8+wQZcYaEK5Cgo8DB2rBIYjY2CqCj9hFWon1DGKu3oaJ2jRzUiIvReB0FWlmTZsi50Xa2P6boakFarEooREZKpUz3s3m3BalUCREodi0VZxS0tgpwcAMnu3ZawBpvvfkqPBy65xM3YsdIroAoK1LsmJuIVlLNm6bS0qElPS4ugrg7cbo2bbury25e5cKEHj0d4XedtbZwIRBqadaNAd9+UKTpHjli81pnNpqNpguxsD3FxkJ/voaFBCcyWFhUd3dys0dKiDZuQWrLEjdOpLIyzztKJilJCf8IEz4l+1FMJ+LZJQoJyaxcUeEhKUhMPm02wbFnXsO6BDVQYKSlqYpecrCZJOTlqEpaWptahW1pUG48Zo7N7t4XJk1v7zMNQEjabCnDRdeV1mDhRH1BAy0COiQxUVKmpEqdTIzVV9+sTS5a4sdmUxyE6WsVMeDxw8cUecnJk2F6G0b5/+YyLSq2srGTSpEm8+eabzJkzx/v5Aw88wMsvv8xHH33U4zdlZWUDyquqysazz6axd28Mra0a9fVWkpI8dHZqNDZq6LrSjFJCZmYXmZldeDwSl0ujrCwGj0dgs+nExHhoadFwONx0dmp0dQkaG63ExrpJT+9i6tR2kpLcLF5cB8DGjXbcbklxcSK1tTZAkJ7eSU1NBHV1NnRdMn58B8nJbg4diiIpyUVsrCQmxoPD4SIlpYv0dDcXXeT0vsfGjXZiY93ExOi0tWm0tlqZP199v2tXLIcORfLeewkIIdE0VT6XC+LiPOi6mlEnJnqIjfWQmeni61+vJi2t9z2XVVU2Nm9O4KOPEkhKcpGX10ZEhKS11YrVqhMZqdbFnE4Lb7+dhMcjsFolSUluGhqsSAl2u4fbbqvoM69Q+e/aFUtDg5WkJDeTJ7eya1cs7e1K6QMcOhRBSUkCbW0Wpk5t5eKL6znnnHa/3wohOXw4EpdLw+US2GzS214DKVcg69fb/coEsGdPFPv2xVJTYyM11cW0ac0kJnrYsSOWqChJV5dg2rSWHu3o+65DUTZfdu6MZt26ZKqrVZmmT2/C7baEzDOw/lNTO6mujhzWMgYSrO+XlsZz1lltZGWpvN95x05Tk5XWVo3ERI93HMXFebj++qqw8xls/QfrBy0tam3WGMv9KUOo+q6qsvHSS6k0NFhITnaTk9OB3e7pV14jTV8RrWfsPkYh/Gc2UsoenxkMNCw4JkYgZQezZkVit0sqK2H37ghcLhXSnZKiZl0uF0RGWrFYVFSY1SoYM0bicoHLpZGRIairk9TWRmKzqT2LZ5/txu2GioooLrvMeiKUOp6VK63k5qrFcik1amuV7z821sbnPqezdy989pmFzMwY8vI8fPvbHo4ejQoRNu0AYO/e7jQNmprA6RzD4sVu5s6FlSutjB0r2LfPgscDx46pKEKXS/L5z3uYM8dDSooFXYfq6hjmzo3ps/7y82HuXCOMPIqamgQcDondvp+ysnxSUyWaBpmZSgB0dAgaGtRa1PjxEilVu+7ZU8CECf2fjRr5d+NgwgQ1K4+LU/UVFwc5OUZ9xQAxVFQI9uyxkZiok5Wl6lTT1NqM/zaS+H6VJxTvvWcjK0t6PRAA6ekwebJyI7e2RpKQEAlAWppgxw5BV5cgNzfuRDnsJ77zDYF3DEnZfMnPh698xfjLBvj3gYqKVL99pNXVgpyc7jqsrdVYtMi3HYe+jMHKPGFC9zaG3FzJ3Lk6paXx3j4wbpwFp1Oc6ONKpDY1QWwsQFVY8iNYX+svwfqBGm+C/Py+0+tZhm6698SqPnXddb5bZRJPWMr+cmM4Ge7tGmecYkxJScFisVBdXe33eW1tLQ7H0DWo4daw2dqIjJTebRmaBunpkpgYid0uOXBArY3FxysXl66rBffYWDh4UAWtJCSoPWipqTpZWTp792ps2WIlNlbnggt0Fi/uXseoqRGkpirBkZur3CHjxql9k+XlahvI4sVupk3TaWmBo0ct3j121dWq0wceFOCbpkFcHH6BDzU1gpwciduts26dBV1X64y6LomK6v7dQNauAk/GKStz4XQqF5ehrHNzJcXFGvX1eNegOjoERUU6NpscsmusDBdQaaklZH0Fu0orO1snNha/thoqDHef78TFqOfAE5JsNklenuDSS7tGjdsK/Pe1paZKNm1SXpWMDImmyRG9jizYyUzp6d19YOJEnZoa5QnQdf9TqNraTl45e+sHgyGwbVpaoLTU2qfcOJU54xRjREQE06dPZ/369Xyle/rK+vXrWbJkyZDlYwjHtLQuduzQ8HgE0dFqPaKzExwOFcEYFaWCdOx2SVOTIDlZRe5FRcGcOS7271ch0XPneigtFaxfbyUxURIfL3E61Ub0efM078Zz38GRkiIpKvKwY4eyQFtb1fqjy6UsK7UZXm3i7U1gG2m6XGrrRGOj2oIycaLe45mGBsGkSToxMWoLx9GjGiA5cEDDZvMM6Ni6wI3HdrstqMDPzdXxeNResoQEyeTJasO/MWseDP05pSOcicRQ0tvxgOEo8tFA4GTC5RLY7WrLQkqKWncczjrsL4HK0ugfgXU8wFWYATFcx0SGujO1L7lxKnPGKUaAW2+9le9+97sUFRVx/vnn89e//pXjx49zww03DFkeNTUCiwWOHo0iK0unsVHD6RS4XHD++W6mTFGdd9UqK1Yr5ObqHDigou6io1VwRkqKxGaTzJihrMJt29SGW4tFKaZp03RcLnj+eRuVlZ4eLqjOTsHu3erkm9hYFUE2YYKyXktKLBQVqUCGvoRNYaGHF16wUV6uNolHRCilXFOjDg/Iyuq2TKqqVPRmWxtYrYKLL/ZQV6cUZFER/RbKwWar27fbmTCBHgL/6qvV362tQztrDlaG1attIYMFhmvmHoq+lF+4Z9FWVdnYu9c6Ikd0BU4mEhK6DyMwGE3h/IGMhvN+h2sSdLIneqOBM1IxLl26lPr6eh566CGqqqqYNGkSL730EjkqlHFIcDgkxcUWoqI8pKV1h/3ruiQvT3qjscaM0UlPV52upUWwY4faKDxhgvRGguXluVm50sonn1jJypJkZurEx6vfOJ2CkhKN88/3eIW2EOrUlk8/teBweMjMVC7agwc1ams50cnVbPzssz19CpusLHU6SV2dTleXikibM8ffRWkMyiNHlHJMTe222FJT8Sr3/hJsthob66a01MLixe4gwmjoZ82hZsyh3HqDmbkP9PzIwQpmdbqLndxcwlL+Q03gZMJwjdvtPd2TZwoD6QvDoaBP9kRvNHBGKkaAG2+8kRtvvHHY0i8s9PDqqzYiItQ+PGMTfGGh2iNnKImKCsELL9jYvl0JgdxcDwcPqmPDOjoEhYVu7yJ3ZqaO0yloa9PIy1PK8bPP1HmegetZu3ZpLFjgIiEB1q61nDi5RW2XiItTrtqqKkF0tDqi7dlnbb0OPl2HefP0oAv7BllZku98x+V3BqKh3Acq0ILNVmNi9B5h4b5CxGZT1kZbG0Myaw4sQ12d4MABjaNHVRkC6yzUzB1UkFJvB2L3xzIdSkpLLcTGusNW/kNNKNe4sYl/tLqAh4uR7AuBnE43+YTLGasYh5usLMn8+W42bwans3vNy2aTJ6LVup/ztcYcDsnnPuf2PldZqXmtlfPP13nzTSsgOXZMrRvW1mosXerfQY0TKmbNUmuA6kQNQWoqdHVJIiMlVVXdJ8pERUlvhw81+PqaNQ6lYvJN6+BBtU9w3Lju37e1ad4AG+P5QCEylIcv+757XZ2gpMQCSLKydO9escC8gq1B9SXo+muZDiU1NeqINl+CucuG60aEYJOJq68eeL8Zbbc19JeR7AuBnCrr1EOJqRiHkUsucVNR0UlurqfHTMt3EH/6qcb06brf6Si+1phhreTl6Sxa5GbrVo2KCo2zzpJ89asu0tPV7+rqVHBMVZVSSkeOCMaNk+TmqhPx29vVgcATJ+pkZakNxSrqVeXZ2+DrbdY4lIopMK2ODti61Qq4yc5Wabe2Wv02gg+3EPF99wMHVDARCPLy9LDzCqeMI7mW43DIE+/WTaC7LBzlPhjlNBg34GiysIaC0bauNxrWUE8mpmIcRpTV6MTpHNPDpfbCC+q2hc5OFcBSWytYsMDjvTbJVyj5Wmp5eTqpqd2h/4ZAaGiQ7NljObEPEqZM0f0USkGBhz17LCQkqN/OnetizRpr0DMyQw0+m03y/vuWEyfkdJ/KsXKlNajQX7vWSkqK7JeQDFQg48dLwE1lpSAyUtXJ/PlO7947GH4h4jtjPnpUnSSUl9d9xVU4eYVTxpFcyyks9LB9u5WmJkK6y/pS7iPtCh4tFtZQcCau640mzrgj4U4mFRWCXbtieyiGtWutlJd337aQmqpTUaGxdavFe/zS4cPqEOx9+zQ2b7Zy6JAIelyXIbQrKwUul9obOXOmh+nTdWbNUgqlulqQna2OWcvPV+tzpaUW7w0bvgQbfMbdkh98YMFuV3fNuVz+exgDFWxnp7qqp7/HUwVLKydHMn685IYbXCxe7O5xIsjJOJA4K0uyeLGbyy93M2lSt1IMN69wyjiS50cak7jejujq60aEwAO3h+OWjlCM9tsa+stoP0v0dMdUjMOEMXtWa3v+imHbNhVoY9y2kJYGkyZ5qKhQSqyjQ3jX/s4+W2fiRHUu6MedxtIAACAASURBVL59WlCBlZWlFMeXv+xh1qxuoe2rUAoLPZSW+iuqmhp1JVVvg6+iQnjvX0tPV3sw9+61eK9gguBCf/duDYfD028h6XBIjhxRV/qsXWvlww/7Pgj8ZAqRgeYVzu9G+vzItDQ18ViwQFlYa9ZY/Q6i7ku5j6RyGu23NfSXke4LZzqmK3WYMGbPQnTfcWe4doLdqhAfD+PG6dxwg4uVK61+a3/jx0uSk91+J6cEruUY1l8o10uo01g6OoR38IU6xcXtlqSnd1+ZBOoevMhItbYZbP2xulrjwgv9XVjhuBwzMnReeSUSu10nKUmd8P/ZZ1Zmzuzs8exwRqKGYqCBCOH+bqTXcnpzh/YVnTjSruDTLXJyqPvC6RScNNyYinGYMNaUjFns/v2CrVstHDumeS9szcvTvXfMOZ2Cz33O4/dbX3yVSjDhVVMjkFJt7A8mGEKl2dbW+zFl3XcuihNKEe89iUVF3RvIA4X+/PluIiP98wtHSFZWasya5aa6Wm3uttslBQXq6CnoDk4a7kjU3ggmsMIROuEKuoEKsKEQfL2t1S1e7O5VuY+kcjoTIyf7w+kWnDTcmIpxmPB17ezfL3jzTRs2myQrS0WC7thhITZWJznZ2LMlueQSt99vh9L6G+hs3uFQkaH79qlozOhodY2W1aoEYaAwXrBAbfY3BmJ/haRx5qoKulEEO9JtNAVbDKXQGWhaQ1WGviZlvSn3kVZOA7GwzhQrajSNFxj99W4qxmHCmD23tmp8+KEFm00dyZaRYXRON21tgilTPH4do6JCUFenAldSU3UmTdKJjJT9tv6Mjrdmjep4GRnqNPz+KqrCQg/Hj9soKPBQVSU4flxgtap78IBehfFAhGQ457IGq4O+Nt33h/4O2qEUOgNNa6jKMFh36Ei7gvvDybaiRlIZjKbtH6eC9WoG3wwThmKIjpYn3KfqPFRD4GRnq/U6I9LS18qKipJceKEbKSUbNljp6BB+naavQINgF5Yap+EbFmVHh1qXCwywCPUe2dmSceMkl17q4Uc/6qSoSO8zCtGI5PR9x74oLPRw+LBGcbG6dSTwXFYD3zowNt07nfhtug/39nJffOvOYoHiYgu/+EUUzz0XOr2hDDoZaFpDVYYzKRryZEbRDuYS4aFgNAUnjWT0criYFuMwkpWlLu2sqMiiqUn4zcIbGtSt9L74z/olF14oaWpS+w59lUpfazl9nYbvO2MLPPHG+H3grDaYFTAcs9BwzmUNrIOBbroPhlF3Lpea3UdHq+00u3cLXK6BnQrUHwbj8h5oGQxLZufOMZxzTt/XkPWH0ewyG2j/Hcg7jbQrczQFJ40m6zUUpmI8CSxZ4ubRRyMBnaQkpRSdTo1vfKMz6Ak4SsgrgnWY3tyUFRWCdessgApcOesstX3DN51Qg3TtWisul/Bzcbzwgo3U1MALdge3btkXwc5lrakRbN2qUVMjcLnsLFrEoDfdB8MYtCUl6jbymBiQUgVHxcd7+n0qUH8ZaFoD/Z3vJCk52eX1LgyFW2u0ucz6G8kdKo2BvNNIK4ORXv/15VQ4vMByzz333DvShTidqa+vZ8qUZLKzdQ4eVEe5jRkj+e//dpGeLlm92oYQkqQkNej277cwZoz0RoA2N+PdVO9LQoL6rLBQp6BAKTlj0DY2quAYj0cdFGC3Szye7nSKiy0kJantFwY2G2zZYuGss1RaQqjbPrZvt9DcDJMnK/fPzp1W0tL0E7dcSHbuVGe32myqrM3NGvn5Hj7+2EJxsYWKCnXlle8g6IuKCo3WVohUl85TVyfYvNlCbKxk8mRJdXUrhw7Zyc/XmTFDKdDERImuC3butLB7t4WKCjX41USj/3mXl1uIj1f10N6u3nX8eHWKT2Fhz7ZIS9Opq1OK226HefPCcx0HMtC0Bvq7jRutCKHap6WlmTFj4gFJXZ3Wo8/1F9+0hTDac2jS7i/G2DDGWmurioBubFSXgfv233nz3CH7a3/fqb6+npSUlB59GkKP7eEimMwYCULJjd7qPRCjXocL02I8SRQV6RQVdfl9FniU2pQpkuJi2LlTXUzcX8tj7VorBw6oS4hragRjx8oTnVAdut3XfjMp8VunMu5f7OoSPfZi+l415TsLzctTt4G43Wqv40cfCd5+28qyZV3ey5T7ItD62blT89aPpkFcnE5cXHc5wrkvMlyMvCMi1J2SSjEKJk/We53VDmXQyUDTCnZweW+3ecDwBjGNtJXky0D38QYy0HcaTa7MkWY0Wa+hMBXjCBI4yFJSJLNneygt7f/6jrpPT0Wyjh2rlMORIxqpqRATI/1cPaEG6fTpup/CNCJCExJCu3YDhfHKlUop7t2r1ufS0yX19bBiRQTp6Z1hvUvgwOnogNmzPX7HsAVuIQhnXTIcjLzXrrWycaMVh8NDYaFKK5ggG61raOG6/HwnSU6nlaNH+745JFR+gfUwmlxmA93HG8hA3+lUUAbDRagxMpqjl03FOIIEG2SRkZIvfMETdLD2JoRLS9WlxEII7zFz8fE6UsKcOeHfF+irMA3La86cbksvmBAIXCfVNOFdnwNITobjx3tXUsHezaiDlSvVUXa+BJYjnPsiw1ViWVmS665zccklbu/zxsHrvs8P920TgyHcYA/fSdLhw5FoWv+DmELVg3GX6GiwkoZKSQ/G8utLGYzWSdZgGG3rzOFirjEOM735wvvytRtWYHGxhe3bLbz/voWYmO41Et/1vuJiC2lpcOSI0gxWK7hc6mi2a65x0dzcnVZFhUZGhiQjQ9LUpFyOTU2CjAxJfn73OpXDIYmMVAedB5bPSG/lSiurVtmIjFRXWlVUCD7+2EJqKkRFqfdsb1d3Qtps9Fifg+DrP72tZR4/3oqUCX5rEn2t4fSVRzD6WpPpa71pIHkG1otvm/VnrTbUOnLgGqnv2uSWLZKxY21MmdIdxBT4m2BlKi21BK2Hzk7BvHnuIVl7HSxDsa4F/V/LDXctbLB9ZbQyXOvM5hrjaUbPWWHw0PjAmdamTRqNjUqhaZrsMZt3OFRwTFGRhwMHBE6n2qc4f76aoQbO2v7xDxtSQk5Oz5mcr7VqlDeYZRkfr9PUBJqmXKdxcR6mTJHs2CEpLxdMny5pb1drdNnZesjZeV/WTaCFGx0te8w4B7qFZTDh8mVlGk1NalKRmKgigJOSZJ/Rv+HkOdiZdqCFVFcn2LlTo6NDWeC+1ohhyRw75iQxMTqkVRWqTM3NPQNIDFf3aHGZDaUrczjeaaj653BanQNJezStM/cHUzGeRIIJllCh8YEDxeVS2y/KyzVSUtRma98OZiiG+HidGTO6zw413IGBg662FoQQTJnS/VmwgRhMCPgGDTU3C5KTob1dlW3mTA9f/KKH1autHD+uhGp2to7NJigsDD7Awxk8vuUoK3OSleXwe74vwTfUA7SiQnDwoIamSZKT1QSgpMTC2Wd7GDt28HkOVlD6ThQ6OwVbtqjN07Nne0KuHU7+/+x9eXAb53n+8+0CIECQBEjwFE+JoihRRyRLliVbUhzLli3btZvGSROnsePWseO0TZPMNFc7aTOTdNqMM5lJM0mrtLHr5OcZx3Fd24rj2LJdy1JkXaZunqIo3jdBgCAIArvf74+XCywWu8DioCy5emc8iUBgv2+/473f520JoL2dMGnTUS76+4UljyXmguErZyiGCmVZcpfl6KgVHR3JE6CA3JzPpXRbZvrsqynOnA5dR765gpQO4oMWyURJgJmZiV0U9QFL1qZGDxUlHKYmyQBZE8ePizh61IK33hLT6plYVERCweGIza2yUsZnPhPG3r0S6us5amsTLTw15QqVIxnSTq6RP1pbRaxeLQFg0fcHONraxChKTDZjZotkoz4Pp04JcLlk3HILJcQYnbuKinDSVkdGc3K5sKRoOblEjbmSCDTkdnYnHUvJHD5zRsChQyImJ/XvtxlaSkSZdJ+tvFeqfrJXK123GK8gpaMVajWtxkaOw4cFWCzAsWNkFVksiGKWAsYuHj2tzWrlYCwGp+ZwcOTlcXDOdTVBtcbe28sQCgH19QR+fvKkgIkJyvDbv98SnVem5Rm5StJQz1kQaJ2Nuo+kSwrYeWFhzHXtclEGby66TeRC01bOg3Lu1IlJRucumZvQaE4Uh5WWLOPyasCizYRaW0U4nRHDsdRW2KZNMo4codrf7dulBHxkM7SUbst0nq1+r+ZmGQ4HKYzBoIymJvmayMa9LhivIKXD7LRM1WrlKC2VF+ORQFmZjIoKjtZWCyorjaHc9J41OwuUlgKcU42j3U7fm59n2Lw5scxB60aZnweOHrUAiKC2lqOiQsbbb1tQVSUnzCuT8gxtrFX7XmoycrHpuX4YYznr2ajspcfDo4kqPh/gdJp7r1SUS2XhSmRkLmUsMZcM/0rGvMbHGfLz9WOvQLyQnpwECgo4OjoE/M//WHD//ZG0XaBL6bZM59la5UOvn+zVTtezUpeY1NlT2sy4vj6G1lYLJIksN3XWoV72m8MBNDfLWL+eo6aGw+0GAI7OThEdHaJhRpves+64I4L162UcPChClmlua9dSNqI2E1GbWeZ20/dHRhisVmBigmHLFgk7dshx80on8ywZko/2vRwOL+rq3Ekz+fQyJe12DpcL+NM/jWSE/KHOyJyfZxgaElKipmSKNpJLNB2z5y5Vpl8u55QO6SEhnTxJLv9IhMHvh2mkpWwRaNLJFB4cFDA2FoDHE/M/q8dSMoenpuh98vKAujoOxjjKy4GmpvTOaK4yb7N9tpmM6GwyroGlz0q9LhiXmNQbqGYsXV0CurspTtXYmAi3pnxfzVRbW2MXSYE+8/sp23D1ailpSrQegy4qAiIR0qBXrDCGodM76EVFdNgfeSSM3l4By5bxlKUB6ZJRqvfAQAibNzuTpoKPjzNT5QpmaXCQ4dlnrejsFDA0JMDrZQiHsQilFhMSyrwzvfBqSiVUzTIX7bk7dUqEKHIEAgKGhhi6uwU0NMgIhydTMpsrCSumvF9np4Bz56h1WzhM8IDz8ww33ihjcpLhxRdtKCykcqFUZQ7pCo90Sqb0xjp+fAFOZ75BORYJ6e5uEYxRt51gkOANq6vltEsa9BSXpiYJXV2ZwzMme7aRUrQUpVNaui4Yr3HSbqDCWCYnGSorZVRUmK/vGRwkqK6zZ+kiFRYSIPnFiyJaWuQ4N56RENAy06oqGZcvE9qJEaNIfdBTX4RMhIWR5tnXN48dOxxJNVOlfCUdyyDZPF980YqzZ0U4HLTukQgwNSWgpobj858PY9UqGX7/latFS5e5KOeus1PA8DC1QVPeY3hYwMICQ23t+JIym3RI/X55ecDAAMMf/mDB+fMiPB4ZN98so7SUo6uLzq4kATU1POU9So/Bx6/xyZMCRkYEVFdzOJ2p7ywpbCOQ5ZK4sQDECfzRUYayMkRLm9auleFyZabEqRUXp5Pj3Xdzdx7NKkWplI9c1DZer2P8EJGZThqdnYIhvuWmTRLeeIPQSRwOukiEUhLB8eMiyso4ZmYoCUSBhtOOr1cukqrNUKp4l7Y0oK1NwNiYgF27Ijh5UkBrqyWjFHKjuEZxcSTp35V1SydGlyod/fRpwmFVrGrqukGfK3SlEzsyGSvZe9x8c06naIqMYsTa9l9FRfS3c+cEWCwxTWhmhqG4mMdla6eKGZpFoHnzTRF2O7B2LYMg8JQlU3pUURHGjh3xdcHaxJTf/57YcEODjJYWCmf4fNnHBs2ekWxKYYx+eyVLp5aCPlTlGvfccw/cbnfcf3/+538e9x2v14vHHnsMdXV1qKurw2OPPQav17vkc9OmidtsHEeOxKdn9/dTbZxRend1NUd9vQyXi8PrJfi4zZslNDUBZ86ImJ4mN8z0NCXHVFUZ93tUp1wrfRqNGgonKwVR/31+nuGddyzgnBot2+0czzxjQyTCM0ohN2qa29IS0P375ctkVXR2CmhtFeMaM2vnrKVU6ejcgE+oP1fKGZTylwMHLGhvF9HVlftrlmk5h5n3uFKUrHRCeb+enlj7r/x8wGYDGOM4fVrE8eMiensZTp8W4jJus0k4Uc9JeebJk3RPU5VMmSHtOWto4LjzTrKkVq8mgIhclTSYOSPZlK8k++2VLJ1aCvrQWYyf/exn8Z3vfCf6b7uCS7ZIjz76KAYGBvD888+DMYYvf/nLePzxx/Hcc88t6by02pteJ422NjEaKwT0NbxVqwjcWW0lnT8vYP36MNxuurRuN8eqVWQFAjHhmI2mlkrLrq6m7Mw9e8Jxc1O6bNTXxy55OmPqaZ5zc+GEv3d2CujtpVhrXV1y8AQ9SrU2GzfKeO89SuhRrHWvl2Hbtth7lZVx9PezKIC6200A6lNTQtpdPlJRphmIZt5Dj5YCUSWZRaO8n3KeAZrr8uUSvF4SiDfeKGHZMo4LFwSMjpIwzaTMwWhORUUEa+dwkJXY2Cjj8GGyuGU5s0xhvXNWV8cRDMppdfkwQ2bOSCaeByOLOtVvld91dgq4fFnAmjUSamt5VhnXS0UfOsGYn5+PiooK3b91dHTgwIEDeO2113DTTTcBAH70ox9h79696OrqQlNT05LNS7kQk5MMPT3UB87ppH8rl6G+XkZdXXLBpeciHBsT8NGPRqIHXmkd9P778a2DlhqFQu/Sl5VxjI3FW0zpjKknkLu66H/VzNrrBdasIUABIH03Zqq1uf12qgecmEAUbq+xkeP22+MBufVc3atX6zc4zoYyLedI9h5zc/q/WSpElWTKyJ49Ed32X5s3y3jvPRkeD8PCAlBaKuOP/1jG4CABGdx2m5SVUFHPSanRtdu5aq3kxTOdmQBLVgua61IGM2dEbw9CoVhTcK0SpD4Laot682YpoSG6mq612sYPnWB84YUX8MILL6C8vBy33347vvGNb6CwsBAAcOzYMRQUFESFIgBs27YNTqcTR48eXVLBaGRNOBzAnj3kati/34LZWUKlUYSnzcaxenXM6tOzonbtiiAvLyYUT57Ubx2UCTNNx1LQu/QVFeTa9fmgO6b2+VVV5NpNNZ6WWZ84QViyBQWx9lTpxC2SxUkVa+8znwknXQvF1e3zkdApKuJoaYnHT81kXfUo0xrJZO+hKBxaWqrYaTJlJFn7r2BQwD33ROKUqxUraK+zFS7qOXk8FKo4d472zukEPvOZ7Bj4lezLaOaM6GHqHjkiwuXSV4KSWdQej/ShqW38UAnGT37yk6itrUVlZSXa29vx3e9+F+fOncP//M//AADGxsbg8XjAVGmMjDGUlpZibGxsSedmxpow23BXa0UpQgKQcfGiAEroSWwddO+9kbSYabqWgt6lt1oZHnpowRRQel8fwwsv5OGmmyJRF4vReNqLVl5OcVd1YkS6lqmWEX/0o6RwqOeQShDoubq1iRTprGsyAZppUX26v8vEBW9G8KcSEkbtv9SKoEKzsxSzS9WYORXpAWusXMmwd+9CTiyaXIKZmx0v2V6nagquVYKSWdRKbFRPyF8LCTdqYl6v9+qxX3Xoe9/7Hp588smk33nllVewc+fOhM9PnjyJ3bt343//93+xceNG/PCHP8Qvf/lLnDp1Ku57GzZswCOPPIKvfvWrus/vMlKl06Snn67A7KyIQECE0ymhrm4eRUUSpqasuOWWGVy44MRbb7ng91vgckVQVhZGXd08LBYOh4PjYx8zThIaHbUu/t6NiooF1NXNw+0mASHLwNSUFX/yJxNpzfftt90IBhkKCtT9GIXoXJQxp6ctKC6ORJNitJ9VVOhrw9rnnznjhN8voKhIxvr1gYTx9N8zBLc7Aq9XxLlzTkQiDLt2zWBuTkAgYMGuXV7D8TN551Q0OmrFwYNuOJ0R5OfLuvPQG2NoyIbxcRuqq0Nxa5nqWanmYnYvklG6a2JmDbKZo97zh4dtABiqqkIZrVW2c8o1Xck5qMfq6HBgzZoASkpiMWc1/9CeBa9XRGdnPsJhAdu2+Qznme29yjWl8g5e9RbjE088gU996lNJv1NTU6P7+aZNmyCKInp6erBx40aUl5djYmICnPOo1cg5x+TkJMrKynSfAaRexGSkjl1u327RWBMu+HyAzcbQ3l4Bl0tGdbUIm02BZyO3oNJwt6kp2RyBHTuAZcssCAQcKCpyRf/m8wFVVUBTU3Facz90yIrqaq7b/Dc/vxzt7dbFOZPG3t4uYO/eMHbsUOtaxnPWPv/CBRENDZRwsWyZK268pqYyDA4ytLdbEQwOYfVqJ2ZmCjAwwFBRIaGlhSM/n6GrS0BbWxE4p0ST5cuL09LGk72zev2NLKKmJmD58tjfGhuVv7kNx5icZJicFCFJHOvWSdG1VOJ/WuvT6y1NKAHQuqPPnRNx8KAF5eUy1qyRkZfH0d4uYPlyfetkcJDht78dg9W6LMHays8nC7egIGbZcS4sWriJ+9vRYUFjY6LVrJ03EDu3MTI+L+rfaNfY7Waw21OvlRnKZE5GlEnugnLOtXfLaO+yJfX7UlPwgoR1VPiH9iwQiLxyFvKgXSvlbM7OJibcJDtDqWipc0KuesHo8XgyLuQ8f/48JEmKJuNs3boVs7OzOHbsWDTOeOzYMQQCgbi441KRkevIauVJ/fb9/QzDwwxPPWVN6SJKJ4aRyt2VLAakrjM7eTIWDz1wwIKHHzan2WqfX1TEMT1NdWna8YCY+3RmRkAgwNDeTigkoijixhsjmJ1lWLaM2lwp754KEF373mYSlFK5QtXuq1iLIxYVhGfOCIvZfFSz1tMjLMKAxbuvjh4VsXt3fLao1v1k5I52OmWUl8tgjNZt82YpWn6ida0pzwgGyV2v9z7puP9Suc1y2UJKoaeesuqWJlytrrpkdCXrYbVkxr2d7CwYAfdfCwk3avrQIN9cunQJ+/btg9PpxMLCAo4dO4avfOUrqK6uxt///d9DEASUlpbixIkT+M1vfoMNGzZgcHAQX/3qV3HDDTfg8ccfX5J5GUHCqZEwuruFKIKL3Q709VEXjUCAwWLhOHrUguZm2RTklVlkDzPIKckQLM6eFSHLDO+/H0PhCYfJ6tuyRdKdm5acTo5DhyxobxfQ2SnC7wdGRwWsW0e/1yJmHD5MYx4/DrhceSgt5fD7CdJs3TpKd/d4kiNqpHpvM5BhZpE71GPJMsN774kYGBCwciXHwICA/n4BJSUEHE29MeVo4b3VSvHS8nKui97jdHIcPGjB//t/Vvj9DB4PJYd0dpL0HRoSUVvLYbMpv2NoaOC6aCrK+wA+FBUVmoYUNKJkSEhOJ18SdKBsMVCXivQQWlIhQZnBGl0qMsM/jM5CKqQgtxsoL5dRWUk1jtns93XkG5NktVrxzjvv4N/+7d8QCARQXV2NPXv24Jvf/CZEMVZM/vOf/xzf+MY38Cd/8icAgL179+IHP/jBFZunXjA8WSbc8DDD1q0RNDSYL0NQxkjWkNWMVqqnHa5cSYkQZ85QbV5lJaLMnDGgrCy90gTGsOjapu4CxcUSHA79eq6yMo7Dh0XY7VJ0zMZGGcuXUw2lUUGz2mpI9d5mrCOziQTqsY4di1nCMzMMt9xCe3zqlIDSUmIWSjYtQJr6xo0y/P7ExsErV0YMU+Z9PsKI7eujPpn5+ZT57PUyw2Qk5X3URdfZWFvJrI6lsoauZLZnNmQm8cqs1yLXdaUKZZrUlW5z9auZPjSCsaamBq+++mrK7xUXF2Pfvn1XYEbmKVkm3OuvWzLK5kp1AZMx92StnJ591oqpKYZAgKGnx4LpaRl2uwyLhSMYJI3WbDPd1lYRtbUy1q6Nfaa0btJL4d60ScKLL1phszFwHsOWVMY0w1DMCLVUjMFsPah6LJ+PLXYdISHl8XDs3MnjavZ8Ph7H1PfujbUTUwvpZCnzijt6xQoZwSADwME5YLMZF77nGokkmXLx+utLk514pbM9MyUzikEqIb9UdaXZkvZuFRVxzM+zrJCCPij60LhSr1YyY/IXFZHVdOgQwVzNzwO33hpBczM35SLSc83otV1Su8eMnitJzLCF1YEDliiQtsdD35+cFDA3x1BSQvGx3l4BCwuIax1l5DZK5jIqL+cJv6uuprTwvr4QZNkebZVls1Grq02bpJTtlXw+lrXLzWyHBvUaT0xQL8hIhARBdTWPjrtli2zovtJzW6nXTXG9z8+TS3lujuHSJREtLRJWreIYGwPGx8mavPNOfbBs5X0CgVkUFxfkpF2Rsbtt6VyeV7Lzh1nS3n8zbtJU7sxcgHAvBWn31m4Hurro7tbW8py2wbreXeMaJzMbODjI8O67VtTUyNiwgbBQL18WUVEho6oqORM2ipkNDzNUVfGEFlWBAMP27ZIhcxdFRIWX9tK9956IggKy6CiuSO66cJihpITwNhljaGiQ0dMjgvPk6P6ZCOcVK2T09s5i/XoHGhqoq4KyHtXVPGVbr6YmKWU3kVRkNo6rXuPCQmIS8/MM69fLcfMmyy+zGB6BgAMnTgiQZaClRcbKlTJGRqi/36ZNMh58MIydO43jvsr7XLoUwMiIC/39DIzRvmbTNis23/T7WOr9Nts2Xh8Eae+/WcUg2XlQC9fJSbrfFy+KuHSJ4tQf1PpoeYokAaII1NcrMfXcnavrgvEaJzMbmEwDTGZNJPttfz+DLCOhRdX0tIANG6Q4IWKUCKSQotGOjjJYLIgmdOTlARYLR3+/gLIyyrBct46ShACygGtqZEPN1oxwnppi6O4W0d0toK1NwObNMlyuxFY+ynooDMWorVcoxLBzZyTrZrtmBJlagM7OMtTWUoNpWUZOGw+fPy8gLw/Yu1dCUxNHZSVPO8mhqAgIBsfh93tQU2Mu0csMaRU3AoNnyMuL72Nppu3TUrbxWirS3n/z3gZjhUARrrOzhHLFGAkgUeSYmhI/sPXRUxjvuCOCFStk9PWJOT1XV13yzcjICIaGhnDDDTdEP+vo6MDPfvYzeL1efOITn8Af/dEf5XSSH3bq8GYFrgAAIABJREFU6hLg8zH4fNQyasWKeBixZDEvo5iZy0Wg5MmQdlIlAimkxAX0AKgliWHNGgl33y3F1f4VFABDQwK2bo137Whba1mtFIeYm0M0LvT665Zol4qTJwlCr6KCY3SUGOXq1alhpJLFEvXee6mSGTJNZEj1THU8bX4e2L5dikveySRud+GCEy5XbhNj9GJqtbWyKSiwXCXqLGWiSrpkJhaaKoaoxCAvXqTaTUCpeybIvCtR1pHs/bRj799v+cDKTzKltAXjN7/5TYyNjUUTXaampnD33XfD5/PB4XDg5ZdfxrPPPou77ror55P9MNLgILWaEgSOkhJyTZ48KaK5WUrop6h8X33JBQEJgqy/n2FmBpiYAARBQDBIRd9GuJ1qShX41wOgNhKmy5bJunPr7RWwbFns0iuJJtqsPHXLobExUhxaWznOnCnH8uXJu1WkA5h+tSYzqEmPuSuChYqy47+fSZLD9LQF1dXxn2WbGJMNFFguYMSuxr1NpSyZzZx+8knbYt0rj/ZxVMAorhSZUTquNTg4IIN+jCdOnMDu3buj/37uuecwMzODd955BxcvXsRNN92EH//4xzmd5IeZWlspDgYwBIOUWg9QIay2H5te/7PxcYa+vviehEePWlBZydHUJKO0VIbDQbiGHg9PyTCVS6fXx1ABoF6zhjIdQyG6DOvW6fdNvO++SMLn6tZaRv0ZlT6Lo6MMdjvNo7NTgNstL4KSiyl7xhn1ctTrcZeqF2MqGhxk2L/fgqeesmL/foupXnbpUKqeeem8azIqLo6knZ2a6t3TyXjVPktR+tKZj5bS2dtc7qPyrKefrsA//IMNP/qRzfQzzfRRrK7m2L1bwtatErZulaPegiuZ9Wm2l+O10H9RS2lbjBMTE3FtnX7/+9/j5ptvRktLCwDgE5/4BP7pn/4pdzP8kNP4OENdHUdhoYSLFxm8XnKnFhXxBM3LyC01P8+igkxd91hYSEW2AMfFiwJmZ2W0tYmor5exf78loZ2MWXdTOMxw001S1KJsbbVg06aILlB4ZWWsX6LXS1bs2JiAwsLYZdYrl9i7N4z+fitGR8lSbGqSUFEBzM0BJSURQxQX7TPMpO9nUrqiUCqLxMzvU617Lmov1WQ0ZktLAO3tiXWTRrWAZqwxs/WFJ08Ki02ticmGQjJmZxk4J+SUTGsTzVor6VqWyfZNeVY4zDE0ZIPbTSULDgfHyEhqa9Wst+ODrt006+r+oOeZCaUtGN1uN0ZHRwEAc3NzOHr0KL7xjW9E/84YQygUyt0MP+SkXAKPJ1bgrdTyacnoks/NxeI1Tz1ljX5HAQvo7mY4c0aEJFHXDr+fYWCARS8pANNMwegyDA8L0TmoYdAU7M6REXKfMkZuWHUPN71LX13N8dhjYfzud1a0tnKUldF7BoMMdXXzKChwpRRcZuN7RoxIEBCt2wyFqAluW5uABx+MrUsy5gBIKYWmmXXPRe2lQsnGrKgIY/ly8wI2U5AIvZjaM8/YIAgclZUUu+7ooHCCw4GsGviaFTLpxDNT7ZvyrPZ2CgV4PMDcHPUlbW5ODYBhVpB80LWbZpWOD3qemVDagnHbtm34z//8T6xatQpvvvkmQqEQ9u7dG/17V1cXqqqqcjrJDzOlo02ZueSCABw6JGJhIZbIU1nJceoUsHy5jJKSeMajuJTMMgUzOJhapvHMMzasWUPu05UreZwVa7VKhu+rtRzLyymWEgpJUcGVSrBk0/5IiXNarSTMZ2YESBJl6X7lKwsp1yMVszXLjLNtMK1eg95eKuPRG7O5Ob1koXQYY6qYWiRCQpExBUmJY3RUQF1d8ga+qfbX7P1KJw6Wat+UZ1H2LSWfKehDZmJr6QiSpUjuMkvpnMsPcp6ZUNqC8R/+4R/w8Y9/HA899BAA6n7R3NwMAJAkCS+//DLuuOOO3M7yQ0zpXAIziBjUzZ6gmIJBqnkiaCYSilrGo1zcZK7EAwcsOH1aWKxTpDKF+vrkQN9qpuHzkbDu6kJUWE9OAgMDAjZvRlLtUW05FhbSe3d3C+A8HnxdGUvNoMxaZEZ78MMf2mCxUHp8Xh5HcTGHzwe89poFn/wkPSMZc0jFbM0yY2Xfp6dpz8bGqGxm795wyv6D6TR0XrzGpilbga2QMv9gkEGSGEZGqN52YYGjocG4YN3M/pq9X+m8i3rfJiepqbjXSyhDmzZJUeWUwC7ssNkYRJHD5Uod41foWhAk16KL1CylXcdYXFyMRx99FPfddx/+5m/+Bp/4xCeifwsEAmhubsb9998Pt9ud5CkfflLqkPbvF/Hb3xbihResePttESMjAsrK4otbzRZ3m0HEcDh4FFVlbo4hP59jfh6oraUaPquVnmWxAKOjDB/5CKXOGxXav/eeiLNnqbBf+c3FiyKKi+kd9IC+1XWQk5NK2xmGpiaay/i4gIYGGVu3yqbq7LTv7fPNoajIGbWM7XbEgW8rKCLpIITo7cFrr1kwMkIWY15e7J1kmRhXslrMnTsjKVF20in25pzjzTet8PuJsRYXc7zzjhWFhTxpbZh2DbxehlCI0vuVc6OMWVo6kVZtmNmavFQ0OEiWeE+PiEuXqI5VkhCNN9bX69+JF1+0oqtLQF+fiKkpBpcLcDgS99fM/UrnXZLVEV68KGJ4mGFqiuD5hodlTEzYIIpAWZmMtrZ4JKZrpR5Tj8wCXSwFXZUF/oIgoLy8PEH45eXlYdWqVdeF4qImOz0NvP8+x8CAA3NzAoqKCL5rYIAEg5lLoS30rari2LIlOSKG00mMe8UKjro6Qp2orydrAyABNzVF8eAHHggbouuIItDfL0TjPDYbuYREUYYkkZDVXgYtsz9/ntxkCwtkxSr1j+PjAh54IGy6qFlhbuXlHK2tIZSX5yMUIkDu4WGKnebnxwuWbLsUjI4KOHpURH4+Mb6FBdKKV62ixKNNm+SkzCEVs02HGb//vojKShnr1xNIQH8/uaMlCaip4YZCX7sGdjvtkd/PsHKlHDdmODyZFrPJhDHq7XFVFUdPj4jxcUrsIoxX4I47JCxbJusqMoODDE8/bYPTSUlm8/N0t8jyS78LRTrvouxbRwd1wWGMFI2NG2X09ZGQ37JFXlRAgrDZbOCcrGEtElNFhQy//9pF90kHsSmXdNUJxsOHD+PVV1/Fli1bop89//zzePjhh/GDH/wAg4OD2L17d7QR8P9FUrT0y5cFXL4sw+22IS8PiETIBRMMklBJhWuoRf4YGGB46SUr2toEXY3TyAIpLQUAhuJiGfPz5DZjjOGhhxbQ3MwNmUJ3t4ChIQGFhTHGarHQe6xaxfHII+GEy6Bl9qdOicjLo+LjcJgEWVERFezfeaekmrs5lJODBy2YnfVh2bICOBzA8DAJiGCQobCQa+DyssPlVPpO+v0MkQgtgMdD+KxVVTxlS6ZUzDYdZqwVcG1tItxuQpFZsYK+ryf0tWuQn09oRbKMBMUmE2aj9+5GCo7RHjc1UX++o0dFuFzkPr3lFhk1NdxQkTl40ILJSUAQGGw2RD0h4+MMq1fLGWGGpuu5OXhQhCwjitnr8XB0dtJn69bJi672cdxyixNjYwTFqEVi6uwUDeEPrxbhuJSwfJk++6pDvvmXf/kXeDwefPGLXwQAdHZ24ktf+hKWL1+OTZs24ec//znq6+vxpS99KeeTvVZIHXyPRGIQaoEAg8MBTE8zUx0o1PG6yUnCDxUEinMpNUNmUuPVXRrsdixanKnRP8rKOPLyYu2LALL2rFZuGCfRxnSUlkpK2yxAP+vWbCLK+DhDfj4xPXXWrV680kwMJFVW6xNPLCyWEcgoKyOBbrUybNpkLv6TKlaUaeasy8UxPQ243fqxXoX01sBqZXjssaXJCkwW90u2x/feG8Hu3RICAfNxvpYWjtZWUowcDsKMHRsTsGnTQs7fS0tKHaF2vnl5HJzHz5e61UO3NvHoUQE33SSlPPcfFC0lQMLVCL6gUNqCsb29HV/+8pej//71r38Nh8OBAwcOoKioCE888QR+9atf/Z8WjDEmxmGxkAsOAPLzScjk5RkLFjWpg/wKCoyS3ZZJarzRRTM6oJs2RVBSIqCnR4hedq+XobGRJy0eVzN75dnalkqZZgWWlVE2q0IeD4fVSu5ldfaiIvB8PnIHu91I6Bpu5mJu3iyjsjKkEZ5XJo6iJq2AKy+XcemSBatWRSDLV09KfzLhl2qP083QDgSAzZtj9b9WK8euXVdub/TmW1JCvUV9Pix+RoliGzcmokAlE5hXAyrM4CDDvn1WTEwIqKigVmaUsJUbwb1UvTlzQWkLRp/PFxdDfPPNN/Gxj30MRYtvt337drzyyiu5m+E1SMqFKS/nyM+PYGqKtPSGBhnT02RFTU4yPPWUNWkxvdpKmJmJZZoWFekXxgOZZbMlq0188MFwXFbqtm0Sbr89Yqp4XZlPLrMCN22ScOaMRcV49K1AReA1N8vR72jnZ/ZiflAZgonrGwNRqKnhuPHGkC6ogpau5PyTCT/tHlNnCAHz84gCTqSboV1YKGPLlhi04O23X7l90jvbDz4Y30PT4eBxtcJaoW8kMD9oVBjlDk1MMFRUUNKcUnucClbSLF3NUHFpC8aKigp0dHQAAIaHh3HmzBl8/vOfj/7d5/NBFM1BaX1YSX1hVq6cRzBYgNlZIYotGokAdnvMgjJyH6g10qIijqkpAGBoaSFXovoCZQOUnApw++GHEzX2dNwgZhhzOkXNu3Z54fWWGjLPdNyy2vcOhRiOHhUMEU3MrnG2wNV669vaatFZ38SklEzHHR21oqMjeflHKkqm4Kj3OBRiOHKE+MT27VJcaCAVuDhw9RSNG51t5bOuLi+qq8sAQHe+gL7A/KBLHpQ7VF5OrdKUEq+eHgIpSAUTaOYM5qrcZyko7eSbwcFB/Md//AfGxsbw05/+FNPT0/jhD38I52LQ6Be/+AUikQgeeeSRpZjvNUNKIL+urg8PPujGH/1RBHfeKWFigrRIsyUESnJGIMAwPU2HsrIyvumn359de55MklRy3Sw1nUSUUGgc27cXGyZJmM1G1b735CTDH/4gwunkaGlJzBw0u8a5aJeUyfpmM+7gIMPLLwMFBflZJYEky7Stro61OjtyhNZ5yxYZpaXGWbXJ6IPKiEyHlCQRI2HxQZY8JCPlDjkclO0LUEbz6Cj1Xk3WP9PsGUy33EedqHPxYhiVlQVLtudpW4zf+ta3MDY2hl//+tcoLCzET37yE5SXlwMga/GVV17BF77whZxP9MNCqVpMaUkbr9PTkLNt65JJoW4mbpBUmmSuXH5mNdGqKjkOn3Nigua+bh2PAk7H4N2SowOZRZfJNaqMmrSWcjjMcPEiw5NP2rBxIwkbWYbu2re2inA6I1nNGTAT5+Zx6DDaVmVXgxst15TKu3I1FvOroSqVOO7oKIWBkiXHpBM3TMfq165hdzdb0kSdtAWj0+nEvn37dP9WUFCACxcuIF9JYbxOAGICobNTwMGDIsrLOWprU7eY0pLRBUqHiRoJp3TdUmaEj3osQaD51NVlDtIM0MU7f74Ua9daDF005rNRLVi9WsLoKMPYGMPFiwL27o0Y9jVMhg5kFl3GLGXiZtIispw8KcJu55ibI6AGINFtqayfOttX793TITOM/mp2o+WarkSSSa57TqrvUHExx+rVHNXVQkpBlK5CZ1Yp0K5hQYGMgoKlS9TJqMBfTX6/HwsLC8jLywNjDHa7/f98jFFNbW1+vPdeORijZBVJAvr7RTgc1EzYqNg9HTLrCtVzcxw6ZEF3t4DubgFOJ7Bjh4QtW1IXHadyg2jHOnlSwMiIsFj4Hu8adDp53Fh+P/Duu4nzPHeOmhYLwgwEodDQRWPGPaW4KisqqEB+1SqOUIgjEGBxcHfKOhqhA7ndwMiIYBpdxqybMBNUGfU5OH+eEFkYIyFZUUEZzbOzSt1jvNtycFDA2FgAHk8sRTLdOadDZs7PB1H0nutxp6am0N5enhXQhJk5Z+u611KmLt5sa4eNSBse8fv9KCkpzNkaailtixEA+vr68P3vfx9vvPEGvF4vAOq6sWfPHnz7299GXV1dTid5LZO6K7rfz1BXB9jtEqanKRnHqMWUWRocZJicpMtcXi5jzRoZeXlc1xWq52rr6REwOSlj5045rkyjtdWiW74xPCxELcDZ2RgD/shH4l0semO53RS893jI+isoADo7SWAagY4DdEmnphg451i7Vt2Y2VhjTKWJ6mm2LS0c77wjwueTdC1NIyv09dctcc9qbOQ4flzA6CiSllIko0yseLWW7/VSNxBKnOCLfT6xiOmZqMWbyfY1S9lmK2da37YUCU+pxjUz5lJbx0tlkWbi4jWD55zJHl1pD0PaFmNXVxduv/12tLa2Yvv27bjrrruwefNm5OXl4be//S2ee+453HPPPSgpKVmSCV9rdODAAmpqnGAMmJhgmJ8n7UsUgdtvl1BUxONQVNKhwUGGZ5+1or+fUt5HRgRcvCigoYHjzjsTtTut1nX+vAiLhYRWY2MsAeLQIRE1NXJc4sf0NPDmm1ZUVsqQZXLNTU4y3HijjPp6GcGggKam2OXUjjUxQegxc3NUDwWQJtnfzxLGam9nAFjc/BVEkRUrOPx+PwoLC7PSuvU0W0kiZuByIUFL1mrQksQgigRo3tvLIMukFQPJ0WXSIaPkEiOrRj3HS5cIuHrjRhmRCFmvkQgJPQVLV63F0/qPQJZLskoCScd6MXq/ZIlHWu9CKlSdXCU86Y1rJiFramoKdXUlaeCwpm+xZgt9mEtKZmlms0daD8PISACcF6WNy2uW0rYYv/vd74JzjrfffhsbNmyI+9vZs2dx//3347vf/S5++ctf5myS1zIpXdGLisiSOHlSQDDI4XbzaKf1TFOzDxywoKdHQHExxSxLSzmmp+l2mEmPnplhsNl4tC4SIMY5NCRg69b4CzU6KiASoUt67BhDcTH9prdXwI03StBqqNqxGhs5Dh8mTFO1FeVyJRY4l5VR7zo1GSGKZKoxJkMJMhIGigattiwKCgin8+hRC4AIamv5kqLLmE3kUN7PauVYvhw4coTWc/t2yfDcVVSEsWPHB1+0bRSn0vMumEHVWapxU3V3Ucis9Z+ppXy1xWuNLM1s9ki7hkp96FJl76YtGA8dOoQvfvGLCUIRANavX48vfOEL+Pd///ecTO7DQOqu6MXFHKtWSWhvF1FUJC/G9Mz1DtT72+nTMfBsgCwVzjlOnxZ056IVBjYbh9fLcMstMSE4OwssW5ZYdEwXmr7n87GodZTMNacei2o45UWhF2MO1HkjfqyKCoI7U7v11IgisoyslYps6uC0F5zg7iIYHmbIy0PGNXVm3ExGzOXAAQs8Hh73W+X95uYImAEgq1h77nIxL4VyUbRtxOi9XjqbmaDqLMW4R4+K2L07HgHKaEwzbslMBce10v4p2z1Sr6G6PnQpKG3BuLCwEEW50SOXy4WFhaXHKrxWqKIiDIcjgpdftmBoiLrYP/TQAjZvTizMNtIWAf2GvLOzFEfSksawimNsVivFnebmgNWr5ehnaivuvvsoxqi+aBYLCSyAkHeUDgguF32mNA6O7w8YiUNm+cxn9JixPpbnQw8txP1WjSjS22tFVZV5pcKIzMZQtM/u6kqstaur47DbgUceyYwZmbUWjEAJDh60YM+ecMJv02ny63Zb0dSU2bwUMoNuk2pfjBi9nnfBCFUHSN9qSndczpFTSy1TwZGpkqfd/6oqOZpDkIvMVi1dbZZtMko7xvi73/0OZ86cwac//WlYFUj7RQqFQvjmN7+JysrKaCPj/+vU1ubHuXNlqKmRsWGDDJeL4/JlUbdrhNcL9PWJaG+n/oUOB6Xaa7MeldjH+DhhMVosJLiCQbLgtmyRo3VrWr++LAPBoIC77opg504J9fWJ8YDmZp4QJ9ixI4KxMREAtfnp6iJEjPXrZUgSFQHPzdGclfjB5csidu6MYNcuybAA2ygm0dzME+JPSkyqomIA27cXx8Xc9GIXnHO8/76YVYah3rPPnhVhs3GokBGzzrwzW9CvFxs9eVJEfr6M5maeFRjAqVMyVqxwJJzLdIAG1LEgn48AE+bnKRYty4kxJb2YmhoIQH0mwmHj/pabNkm6cbymJsn0GTA6i0bjlpbSXUoWO0ynC0Q2GZ3pgh1o97+/n+HFF20oKkre3zMbylX/TuAq7K7xta99DX/2Z3+Gj33sY/iLv/gLNC2qmJ2dnfjFL36Brq4u/OpXv8r5RJ9++mn85je/wZkzZ+Dz+XD69GnU19fHfcfr9eLrX/86XnvtNQDAXXfdhR/84Adx2K7nz5/H3/7t3+L9999HcXExPv/5z+PrX//6krXJUmelAsbuka4uAZcvC8jP51FM1PZ2EcGgDLeb62qSNTWALMuYmEAURLmxkcfhRaZyzxhDWiV+Xllp7JorL+ew2zMras+2wFnvHaenOZ55xoabb45kXDdZXc11n716tYS2NhHFxZGcua5SdYVX5qxn1YyNCfjoR+PXT4mN/dd/WaM4txs3ylGcW733cjojpmDzUtXIKsDtw8MCSkpkrFvHVbWh8aAIyeOl2jNh7DLUs5pWrtTPrjYTRzYzrrprTS5g6a6kS1S7/2NjDG63jLExAfX1Ulr3F8g+E/lqo7QF49133419+/bh7/7u7+IECuccFRUV2LdvH/bu3Zvzic7NzeG2227D3XffjW9/+9u633n00UcxMDCA559/HowxfPnLX8bjjz+O5557DgAh83z84x/HzTffjLfeegtdXV34y7/8S+Tn5+Ov//qvcz5nAJietqC6Ov4zPcbi9QKMxccLg0EOrxdoatJ3QZCGKOkWwisuzbNnhUXrUb9oPR1KJsCeesqaVZeAXGK9Tk4yHDokYmxMgMcjpuwKkIxBj49T9unx4wJmZli0X2B9PcWIzV7wVO+nuJnCYSrMdzh4NOFIm1yjZS67dkUSXOr9/Qznzgmw2WKtqd57j8b/zGfCugIvP19OaIdm1v2lB9w+MCCipSUmFCcnCURhYIBF/51OTM0sqo5C2SJCmR83NwXmV1JwaPff52MoLqaEPIX07q8R8Mazz1oxNcUQClF4p62NGhDoCcerDeVHjzKqY3zggQfwx3/8xzh16hT6+voAAHV1ddi4cSMslowemZKUNlatra26f+/o6MCBAwfw2muv4aabbgIA/OhHP8LevXvR1dWFpqYmPP/88wgGg/jZz34Gh8OBlpYWdHZ24qc//Sn+6q/+akmsRnVWqkJ6jMXlIqtvbi7W5V6WiREn0yS1B03L5G02jiNHRNxySwyFZSn8+tnED7Lty6YeW0F8mZoy3xUgmVUtCMCRIyKKi2OW/JEjIrZtkxLid0bC7+RJIQ56LhSSMTKi30vz4kUGu50+m5+nBs9WK49j5kZ7rj4fbW1iFJNXUbYY45iYoPfV26+5OQGNjfHrbdaK0VvDsjIJbW0Cysqk6L4AHNXVMgIBctOSpWteaUuHseaye8OVYuhXahzt/hcVUUa7km0O6CNZ6ec6IJodX1xMd6SnR8CBAxbdBgTXAqWUYv39/YZ/q6ioQEVFRfTfw8PD0f9fW1ub5dTSo2PHjqGgoCAqFAFg27ZtcDqdOHr0KJqamnDs2DFs374dDqXaGcDu3bvx/e9/H5cvX0ZDQ0PO56XOSk3GWFatkuFwEOOmfosctbUyamv1rQQjTVLLoNat4zh8GDh/XsCOHVLO3DN6gXttwk6ycdS/P3tWQDjMYLMJUfzYwsLkmr0R3Bz1auTIyyPXkJmuAMkYqKCf4Jt0DlpQhGeesUEQOCorSeHp6CAYQK2w27s3jCeftC3itHK0tJClK8sxZm4W0q++XsbAAPXwVEjp5Tk+zrBnTyRB4AUCloQ+m2bPXirABGVfAIaVK+UEwalQLpW2bJM9cg2zdqUp2fwT+3ty9PaKaG427u9ppEC++qoVzc2y6ez4a4FSCsYNGzZkZElNUY+kK0ZjY2PweDxxc2WMobS0FGNjY9HvLFu2LO53ZWVl0b8ZCcaurq6M50V6QycuXHCit9eC4uIIWloCmJsLQ/1Yt9uKM2fccLsjWLZMxtycgMuXbfD7Izh/nkV/19xMB3VuDtCb1vnzpSgpCWN2NvbZihUi2tqcOHcuaDh+OjQ6asXBg244nRHk55MwOnPGgpaWWYyN5SV9T+3vFxYYjh0rQ16ejMbGIIJBuqAtLQFIEkNz84TB+NPR8efmBExO5oHzMNraClFRsYD168Po77dDkjhsNhn9/RZYLEHs2uVFV1e8sA6H3ejuZigoUJetkFCZnrZgxQoZAwN2jI2JcDolrFgxj85OKwYH5egcTpwoxOysCEGYhdtNwi4QEPBf/5WP6WkRpaURTE/Ts0MhhrY2jpmZhYT3W7PGjWCQ5hIKAUNDsbkcOhTQXfddu7yoqAijuRlobqbnDA25MTdXgPl5rrJABUgSEA7PYm7Oi9WrrXHncteuAObmxnXPhfrZemdPWcNIREB/fx5mZ0VYLBxVVQuYmVlAW5sbFRULqKubRygkYWgI8HhEHD9ehMrKmeg+BgIW3T3KhJQ7pT4nZp8/OmrF/v0l8HqtCIcpfn/oUBj33juFior05mbEP0ZHaf2np2P3Jd1nG5HeHT10yIaSkgg4Z2CMw+cTMTSUB4Bh9eoA7r9/DmNjeTh3Tv/+6vEWWQampsrh9QYRCsXuTzAoIBRi6Orqj5tTLt83G77cpE2/1lBKwfiTn/xkyRJTvve97+HJJ59M+p1XXnkFO3fuNPU8vXlyzhOEpfbvRr9VKNUiJqOuri7s2NGAHTvUnybW3zQ1AcuXxzQ8t5viTbW1MY2+vV3A8uXJ3Ytr11oQCMRryQUFwJo1wL332g3HT4c6OixobIwfw+cDLJZSPPaY2sLTH0f9+2PHBFRXi5AkIBRyoK5OxtwcMDlZhFtukdDUVJzlqlnlAAAgAElEQVSg+V64MIbGxtK48SsrKQmopQUIBByLWXrUXWJsjPBBH37Yjupqd8J88vPJRUTAxEpndSFqKQUCwLp16nd1YXZWQGNjTHtubxdRUgIEAoVoaSELSJaBc+csaGyUsbDAoho158DICMPatRE0NRXrziUc5hgdpXIViwXR8hW9dfd6SxMK8/PzGWZmrOjpEaLxx2CQoalJxj335KO6ugxNTYg7l11d3ozPen4+oTD19FBtrWKdut0y7rknjGXLxMV9cUV/Q5YKg8fjwPg4oS+RVZO4R5mQ9k6l8/w//MGKmRkRJSWx0Mb0NMPFix4sXx4xVW8sCMDo6CjKyip0v9feboXLJaO62vz9NkvaOzo5ydDeLgKQ0dLCo70wb71VWoSQLMSNN2rHjr+/erzF5wNuuEFAMGiH3R5bq2CQ4aMflaLnKdfvq4THlopSCsbPfvazSzb4E088gU996lNJv1NTU2PqWeXl5ZiYmIgThJxzTE5ORq3C8vLyqPWo0MQEaezKdz5IUscX9u+3ZJTlaTYmlKx7Raq6pmxjN+rf+3zkflSyMDknwTE2JmDTpgXduMaJE0WorKSMTe34aheh2a4Ayd2F5mrbioqoPlSdvKCAJVRUcHR2kivR4QCmpqi8Ruu2VOaiuF8jEY6yMvp9a6sFfn9i2n6ygvIHHwzjwAFLNCt12zYpmpWaLak7xszMUEnB8DAgihwLC7Qet9wSi49mgjSkN166bs1MY3ZG4BmHD4sIh1nKemNRpFi0z1eAO+9EQkeTpey4MTjI8NZbIgDCJ16xQo4qLAsLDJcuISVylR4Z7eFnPxvB229botnxoRAQDpMwVmpXr0SHkVzS0mTKmCSPx5OzWpStW7didnYWx44di8YZjx07hkAgEP331q1b8Y//+I+Yn5+H3U7W09tvv42qqqqE0o8PmrIRPlYrx3vv6YN7A/pB9GeftYIxoLaWPuvvZ3jhhTxs3RpBXV08A8g2dqP+fVERRyjEsGyZDK9XgNdL89+1ixi4XmZhcXEYbW1O3diUIljUgAr33ZdaGCQrW9ETmlrEHj3Iu74+AcXFwIkTFjgcMgQBmJkBLBYCMDCa0/CwgJtvjmg0c47+fiGtda+u5jlPfhgcZDhwwIKDB+md5ucJKm1mhmFmhpLINm2KJXop8VFlHQ8csODo0Vj5iNkxs0nOSvZcI2GrBclQaGLCOJMWiPXsPH6cklEYk3DpEsPWrRzT0xz79lnR0MBzmi2ufaff/c4Km41qWpXks2CQhGFREddFrgqFGI4eTV7cn0yBVEq5uroE9PYK2LxZQl0d8ZE33sjDxATDqlUyGhtlw1Zs6SB/6YFR5JKybjt1pWh0dBQ9PT3o6urCK6+8gttuuw2BQAA2mw0OhwOlpaU4ceIEfvOb32DDhg0YHBzEV7/6Vdxwww14/PHHAQCNjY146qmncPbsWTQ1NeHIkSP4zne+g6985StxSTu5pEwLUdXFvoQeIuLUKTGqjesVLSuXwukkS6m8nCeAewP6Rdvt7QLm5hAtEu/qogxCSaK2TOrCbqNiarOFuupCXwUsQJbJ9VJfL6OggOHOO+lZegDJkYgXly+7UFMjJ4zv9zO8+641JaCCuT2g4vOzZ8W4llxU8xdfrCxJBAxfX88xO0sg48PDDH4/w/w8MDUlYHaW3vFznwujudmYoRuBQs/OskVvSGzd+/oECAJZ+tm2SUp1VpXz1dnJUFBAZ9TrZaisJCxbr5fGN2q35fczdHRQ+czq1Vy34F+P0gUZMEOpAK1HR6kVmxY8w27n2LCBJ+xNV5eA06cFDA5SRvTQEHW6D4fnsLCQD5cLOHOGlKmNG2UMDjJ0d4soLY1Zpbloz6SsVWkp1ZJarTT/oSEGQWD4yEcoVKEGlbfbgT/8QYTTydHSkry43whIQPl8cpKhslJGRQV1xDl7VgRjHHNzdD+Gh2OWuPp9k+2HHli7HhhFLumaEYz/+q//ikcffRSvvPIKAOD555/HU089hVWrVkVxW++44w6cOXMG3//+9/HrX/8at9xyC3784x9HrUO73Y7bbrsN//3f/41//ud/xrvvvovHHnsMX/va15YsjmrEbIxQ9JXPOzsFnDsnIhDg6OwUMT9PcdDiYr6oPSciVLS2iqYYiB7jVXevACjd3+0mV6fymYLYv2uXlFGvNoXUCCOzswy1tRw1NTzaoUKNxv/mmyJOnxYxM8Ngt5NLy+udRXOzU7cLhhkmaqaDQSrGqYeScscdhCa0aZOM06dFdHZS302PBygspLWpqeHYuTPRhRo/tj4CSlUVx86dkbgOH3NzDB5PbvrwpRKMytr29wsoLKRuLnY7wQNWVFCaviiSMrBypZygMGW6N2fP5r57RKq5lJXx6D7MzTEIAp3RNWvonKr3pq+PhJzdzmGxkJJ0+bLSuSYEjycPXi/B4nk89JzCQhJcfj+d/2xQYNSk3G2nE9FGBTR/ii8XFyciV124QP9/yxY5oVdqukJazVtiPUEZ+voEjI8LmJxk6O4WMDQkYHBQwI4dEXAO7NtH8en5eQaHAygujs1BD/nL7/chFCpakl6hwAfsSk2HvvWtb+Fb3/pW0u8UFxdj3759Sb+zdu1a/O53v8vl1NImI9eQug9iczOVb/z+9xbk5XHYbISKc/68EC3raGhQGHp6QMp6rlBt9wqXi4C8leJwIN5tl229VarfKy21lKLwkRGOvj6GLVskBAIWPPCAviBOtQZGbuTycmJ4iguntVVEJMLR0SFGC/vLy+NjIsneIV2AdzWZrVvNNA6trINejDkZKWurYOU6HBzhMDHeYJDWo6xMxsgI0y3tyGRvlC4WucbYTDWX6mrC9lUn0gDkvbl8WcSaNVK0k0pbm4jVqyUUFlJDboeDajV7egS4XCK2beM4epSsz8ZGYuQeD8f27RJaW4VoWZDVyvH665asSkPUd9vjIXAFn48EpXKutchV8/PUdSWGTkTJYe+8Y8FLL1mi4QgtvnOq8Wdm6L3a2wUUFHCUlHCcOiVicJChoSGClStlvPWWJdqSL1ndsRkwilzSNWMxXqukp4UbaavaPohuNxXKBgLCom8euHRJwPw8xWhWr6aDqmjPZWXcFNaiHmbhwgKD0wnY7THX4NmzFDe5fJkO88ICw4YNMezJM2fImjPjxku3z9yLL1px9ixZraWl1PtuZISYy113DWLdOv3MwlR4k9q1n51lOHNGhN+PODdSRwfDwABpvIWFxDyGh6ncYft2fSGifsdjx0TY7fEJOuEwEIkAd92VXAgZYXZqGWWmffiMrGGHw4u6OuOMTWVtXS5y4dpsPMrU8/OB+noZVivDpz8d1sXHTXdvlHsRiTAMDQlobxfQ2Rk7i3v2ZG5dmcElVdyD5eWkIDmd5KWxWjna2ymTurKSwg2NjTzOSotE6NktLZNwOAqwsAA0NMhYtiy2h5JEoYsdO6To87O1/JPhkVZXx/CHN26k/zZtkhGJsDgruLub4aWXrMjLo/nNzDAcPGhFbW38/FON7/cztLfTnVm5Usb0NFmD5eXUJm/tWjkawnG7Kc4ZcytTLbfbTUJdu1cjIwHU1BQsmcV4XTAuMekJRiOGdvy4iA0b5LjPT58mpr1iBcU1AgH6TxTJDQIYAyn39TG0tlogSaTp6jW0VRjvnj0RrF8f+8xmI6QUi4USKASBIxKhXo35+TzarHhgQEBdXXy8yO+PF4J+P/Duu+k1KH3mGYKYI2FNnT3KyogJ3XrriKHLLxVQsdlmzX/4g4iCAmpazBjtTzBISRkf+1iiYNMKm9FRsiScTgXeLxHgPRmZAYXWY+59fQwDAwy9vYKhAqIngKangf/9XwtGRvINf6esrcNBezEzQ/VqNTUy6uo4li/nSV3q6e4NQOs+MECK4Nxc7Cw6ncD69ZkDXOvNxSheq10vtxsoL5dRWclx770RTE7Smnd1iejtFWC3A3V1Mm68UcaOHX3YvduNVatk9PSIuu9uNgRihswqVcnW4uWXLYhEgJYWORq+4Jyjt1fQPftG4wcCDG1tFFMuKSGlXhBIQZifp4blSginpYWjr4/McrsdGB2lGO3OnRFUVSXu1ejoPO66K+96jPFaJT3BaKStzs+TC1P9+YULhArjditCimFsTIgio2g1QuVQdnUJ6O4mF09jY2JAXY/xqj8bGKCsuuZmjhUrOBoaOHp6GObnGVat4rhwQYTVSmgqiuAGKB7a0SHGCcH9+63weCggn+riK1bXq69aMDdHGqSyHuEwuWcWFhZw5oxbl4GnYgzatW9rEyGKiHZ1AIgZnz0rwOFgYCyWfLGwQO4ePYtRyzw9HrLclGbQSozq/vtz13Fcy9D6+hiOHrWgspJjakrA6dMijhyxoLw8XtPXCqDJSbKap6YWcNNNVkPFRRsbXrOGsl4/+ckItm837qCi93szewPQ2ervp5il+iza7XTWBgaEjLqnaOeSLF6bKsbp9wMvvmgDQBbO9DRw8aKI3bsjyMubhMfjSfrumVr+yd4tnU4b2rm1topYv16GK1ZyCrud9ue++1KHT5Txt2+XIEmIxjkjEXK1OhzkSVHyCASBYreKtT02xlBayvHpT5MbXm/tmpuHDL1GuaBrJsb4YSKjGJLSB3F6Olbc7fUytLRIyMsji8PjkXHrrTLm55EQm1DiILJMoORr1kioryemE59Wngg8bgbiKxxm4Jxu78wMi8YfBwcFHD9Oc+3tjXdxFRUBkQi9T319TKAYARQrMaaWFgnnz4u4cEHAmjUyLBZKhrDZKL5VXR0fm9XWWxr1ITTbrHnlSrII9CD6tKRXN+bxcNx+O8WQ1q2TlwRSTJs+PzzM0NwsYXSU4lwkIIFnnrGhsjIUHVsbY+7poeSGkpLIIhwdYBSrXMrYcjr9EJP1oDS7xspcBgcZ9u2zYmKCWqdpQee1WLw9PQJGRxlKSymbcnhYwNatEYyNsei9WLWKzqRaKBm9+9XQp1A9t95eBp8v/m5OT1NNrkJm60pvvz2CcNiKwkIJoRDDkSMipqcpHOHzxTcgT1Z3rF27XCAjJaPrFuMSk57FaKQ9NjdTAsybb1rh92MR85LKDZqbyQVXVMRhsVAcp7FRjsYmtK7Nri4B09Nk9Sl+eyWtvK9PTOnW1NPe+/sJhaW+nmNyksoQvF5KTS8u5hBFcoFIEosbd3qaYXw83jrUi32qrS6Xi35HY1D7olCIYfNmCQ6HD0VFhVEX4JtvWlFZKZty02rXvqyMLPTi4ngX1623Uv/J6mqyVIqKOCKRxKxBRZhTfSKivSndbgJwb27m+NM/jZjS3DMhtXXQ2ytgakoAY+T+Yowsep+PBIuy1oqlOT1N2ciHD4uYm2Ooq/OhtpY2LZXFkm7M2Oy7mO2HmEkPSr15K27+nh6Gigry2ij7p2Q979ghRdfrzJlYhnhDA7lHh4cpc7umhixaxcoZH2eoqxtPWa6Vyz6FycjsnhUVcRw8aAXn1Hx7aoru4Oc+F8ayZTxlxnb8s5JnoGtDOGYz3Je6H+N1wbjEZLSBRu6O998XUVkpY/16OkBWKzA2RnGP6WmG0lLgzjsTSxPOnxeirs3ZWYonhkJIqCnr72fRBJ+pKUoz7+4W0NYmxM1D77JGIhQ8t9tjKd99fQRbl5dHY5WVyeA8ftxIhOr4yssT6w6V8ZQsVKUOrLiYXGY2G8U29+yRIAiU5DA760dhYSEAYux+P7B+vT6DNGqEq05CMNusWe/CGtWNjY8zOJ3MkLmlKtdRf66N2RoxtMFBcp+q3XLBIDE6q5WSHpS6TL+fNP5QiObrcnHMzEiorMxLqDHTm3uymjOzAjPV3ijnUe8svveeBQUFQE+PiMlJKuVRBFkyYa6dt+LmB4yTP7ZskVFRIeOdd6gO0ePhWLdOcU9TEbs2BKKsX2npREoGrhYeXV0C+vvJhT8yIphObktF6QizZcvIO9LbK6CriyARly+P7UW6MVE1r1Mn/eiFcMwqkNcF4zVOZoqm1cyhq0tAVRUxeaVVj9NJzO2mm6S4gn11bKKtjWrLrFZyc7a0yIsacXxNGWN08Kem6NlKMsH4OMPsrJCyTk/R7hTtb2qKEmScTsoyq6xEwriRiIC77gojFGIGsSV9q8vhoKJxQYjVV8oysLAQE4yKq6umJiawFGunvJybYgapipaTXVijurFwGHj4YeOmyHrz4pwnJCkdOmTBuXPUQioVQ3M6OY4csWB+HnGYlfX1Mmw2xMV+L1xgiEQYbr5ZxsqVxNRCoSBk2YHCQp7UYjHKHtWLLxvNNROro7NTxJEjNMboKIsmZU1MUGz14kXSBoz2Sh/YggGgxCuj5A/lPvT2ElpNTU28F0YPeEFZv3B40hQDV4ROXx9lpuflwTC5LRPhmC5IwrJlpKBEIpSJvnw5j57HI0dEjI8LmJqK1RZnW1eaLl0XjNc4tbX5ceZMqa4Grccczp4VYbORphorkKVLoyS4xKyhmLtzYoLFoVmsXElxOVnGYvkFNdzt7KTi2uFhApdWMiZdLqq9Ul+UVAk6GzfKmJ4WMD3NMDcnIBhkUdgp7bgjI0ICeoxCelbX/DzDiRMW5OUBN94oQ5apXGJmhkGSZlFcXAC/n4RwQ4MchbgCiDFJEsOBA6Jh0XCu0rzVe5CfT7GQ0lJyoW7Zoj+G2XIdBZFobIxhfl5Ae7uI2VmqH5ybYwnvUFRE2ZKnTlng85EypZRQKAlGyrO7u2k/ZmdJgXG7OSYmgpiczMf69XJSd5ZRssiRI5SBaIb5JmPUTidPsCQBRFFzQiH6TX+/uAg5JyAcJhzQjRvJvaknQPTmrbj5N26UDZM/9PZaIT3gBbXilw4DV6+JUXJbpmc3kwQfo9ImrxfRDHHF7SxJ2aP2pEPXBeM1TIODDC+/DBQU5OtqxXrMwWKhGqnychkXL1LG5Pw8w9q1coJmpgetpqBZSBJZatpYZFkZMcXz5wWUlRGyTU8PQbKFQgyBADOs09N7v6NHRQwPE/OKRGgOTifw+c/Hj5vMKtCzurq7RXDOsXevhNJSclPZ7fS/jPkxP18EtxvYsYNigdq0+7k5in0axY2y0WzVVv78PNXYKfWfyeJDyu9eesmChYWYtg0Yl+u0toro7SWr2Ew95bJlHBs2SCgooGcqTLu7W4hjjBMTZDEqySb5+YAse7F9ez7uvTd5bMtIQAwMCFi9OhEuTW+9jRi1UQz88uWYUtneLqCigs4DwbaRMHK7ZWzbRvij+hnPifNWu/ldLlImSkpYglAE9LOAlXKocJhh0yYpoX4zHQYe7wES4zxAK1bwOE9IuvFdoz2TJGaY2WtU2sQ5xVgVBfb99wUMDJAg7+gQcuL6TUXXBeM1TAcPWjA768OyZQW6GrQec6AsTmJoly4xiCLHxo0x4F117CdVYFsPJs3ppIL5/n6CaQoGgZoaGVVVlMgyPS1gwwbJ1IEmIGmKC/n9WCyvoNjgzp2SafeNntU1NESWTFMTvffkJNWJnTsnoLrah717HdiyheI8WpevIFAcKBhkhnGjdDVbRajt32/Bq69akZdHxd6yTMpFXh5p1EaxSLV3IBQiZqfFjdQr1zlyhFzJ9fXcVD0loG/paxmj3a4oMTwKSWa2NswoWaS6OhEuzShWmaw0Qy8GfuKEiKYmql9UvCNuN3kitm6V4PdTndz771vQ10cdHm69NX599OPmyd382nU1Ww6lUDoMXIuPPD9PvECJvSqCzKy7OtW7KwqkkZveqLSpuJjCJkND1MqKc1LSOjqEnLl+U9F1wXgN0+HDIkSRMigVUmvQRkDhokiFwzffLGFqiuJLRtZIqsC2Mg+1AFaEz7lzFK/MyyP8wsFBAW43ZX8qRejJMtnUll51Nbl6qRMHvZ9Z943epVW7SJVY6/w8uXAslllcvuw2rMlsbaVxHQ4kjRuZzdJTCzVCXQEmJynjt7iYLBeXC0mzT9VKgsNBVh9AwluJ6a1dK+Htt61obydw7kgE6OsT4XJx2GyJ9ZR1dbLpRJ1UoOfp1IYZZY+uWCHrCsymJikB9F6vaDtZDHxggEohSks5SkoQVepkmc7H6dMWFBeTO57KhkS0tEhx9Zt+P8PlyyRkL10Soolszc2JCT/J3l0Llp1M6UsHK1m9JnoeIL9fSHCJm83G1dszRYE0epb2zAwOUunSRz5CivrEBEG9rVhB5WPq5L9sXb+p6LpgvIZpcFDA2FgAHk+sCEutQavT5vXSwAsLqSxCe5HTrYXT084VNBy7nVwkeXlAU5MMh4PiGzU1Mt54w4Knn7ZhaopFsUSTaZTa9zMDuwXoX1q1i7Sri9YGYFi3ToYo+lBYWKBjeRKzee89EUNDAsrLyarTixulk/yhFmrt7cJirJIYbXU1tfE5ckSMAiP7/UgQBOoi8fx8chnPzdEarl8vo6lJQmenBSUllNU7Ps4wNUU1kLW1ctTKdDopbuhyIcFyePVVC155xYZLl4QozJcSb1ODPygF7U4n4vBh8/KotMCMwmAUf9buY1OThNZWS8I6NzXJaGoyLs3o7hajaxUMkps0HCYrOxSims2REbJOCDqQEr/Ci+VtlZUyhoZiSC1mO8/o35/Mgc31GPjJkwJ++tM8dHbG75N6TYw8QFqXuNG4eqTsWXk5x8iIgDfe0HfpK89KVdp06pQIxuhOXr4saFy/8pIm5FwXjNcwOZ0cx48vwOnM17X4lIOnlwau1OctXy6nfZH15qGnnS9fzjE9zVBVRRfQbifXjSxznD1rgddLiTyU6CCgooLD4TDWKLXvlwp2S4216vPFx2jULtLjxynGtm4daap+vx8lJYVxl04t6JQ4an+/gJoaKnTXxo3SydJTW76KG8/hQBRcXN2yp7+f4cUXbSgqiu9+okDrKUpCfj7Fs9avl3HvvRG8/z4JuYoK6sCxahWPlrcALKGeUms5zM5Sn8SFBY6mJgJjHhmh+k8lUUfNGPVivw6HF6JYbFph0CMt833pJQv8fgaPBwmdG7ZskQ1LM7q7BbjdsczaG26gzg9Hj4rIyyPX4kc+Qhbb8DB5AoJBcmnX1ckoK4tHasm0dZWRAqXdTyCm9KmThy5eDKOysiAu4e6nP82DINA51e6Tek30PEBmlU0z72Pk0tfDi9UrbVLjv2qT/+x2qjUdHKRYdq7jjdcF4zVMdAlHIMslhvELozTwVPV56c7DyPW1fz9p0UrsKhgknNSFBUAQGAoLAZuNnuP3U0cPI41S+37av6tht5JhrWrdxIJAsbeYpeYHUBh3gfXiqD4fMZLm5kQMz8OHRcgyw4ULItraqBausBBRN7Ca1MzIbo+58VwuYsrqlj2dneS6lWUW18cyEmEIBsl9qqdE/Pa3FgwOCtG5KHV5s7MMd92VmPGotRzOn6ckKIuF4tMkUCk5wmpF3DvpZRt2dAg4dMiK7u58OJ3cFHyfEamZ79AQCfH+/hjzTWZJKGemrY3eVxTJ5Tc8LODcOaqZvfdeCTU1fDETmaOnR0BZGWG1ejy05lNTdAYUizFT2LVkwOZ6+9nUJMWV3IyNBeLc/tRSjqGsLIbBa7RPepQtGIAZl36yxDF1b9LNm2P4r2rXb00NhTPm51k0ozze05Q9OMRSC8brkHBLTBUVYezYkRxCSw8Sitr2xF+SbDp8G8FR7doVQVtbDPaspUXGkSMiysooOzIYpOQVh4NiN1qoqlQQYUZtkpQO5wCiHc4BGQcOWODx8DioqUSoMAGcUxsmBZrqpZcsqKnh0Q7hHg/Hzp0E6aYHDycIVF5QXEwYjePjDMeOWeF2y+jtZXC5FE05fvziYo5VqyS0t4soKpLh9bK4lj0+H0NxMWniChUUEAC2UffzwUEC/RYEas0TDJK23dxMAkBvjbVnZmaGkijUHeEdDmoftHmzMdyfEr+12+nfExMMMzMCCgpi72R07oxgwdQtu3p7hSi4QE+PAI9H0oU70z7rvvsieOstC3p6hEXoQY7paYJYm5xkcXNbuVJGX5+4uD+UROb1CvizPwsZrhdgDnbNqD2V0X62toooLIwpdwUFMgoKYvB6yvsp9yrZPhmtt8+nKBoU/lC39kq1R11dMQXH4+HYvFlCdzdlpm7eDKxcSaDmr78e345Mrx3Y3r3h6BqoW1mdPi3A5ZKxbh1XtbKKwVEaPSuXcInZ0nWLcYlJq9mkCrrrJZ8olG6HbzOaWVkZx9QU1Yc1NFA9kjJ2ZWUseSUcxqL7zhjNJdWYqdLRfT76bV2dnDQeJQh+3H23AwDScgup6fRpslYdDtLWu7oEeL1UE+f1ChgcJMt5aEhMGL+qiuOBB8LYs0eKxohmZyl5qqNDiCb5LF8e3/1E7SZTEEQOHxbx5pskoAMBWmulOH98XMADD4R1tffz50W89ZYVExPEYKmWFCgoYLBaCXB+aopi1tpnqC1gda0sY3MoLbXrIiZp1zFZjPbddy24eJGe63JxDA8zeL30bsuWybqoR9pnXb4sLpYccYTDbNHty6MlAuq5NTRw7N4diXYUKS3l+Nzn/j97Xxobx3Xn+XtV1exmN4/mfYqkKFLULdNS5EuSD8lHjkkGCztOPDv2ZmaRQQaDzGARzAADDJAA+RJMNl8yu8DkwyS7iyQ72QGcQ0kcR5bXsmxLsiRKlERSpEjxvs/uZpPN7qq3H379qqu7q5uUbM2sB3pAEItsdldXveN//I54mn/gvWZa+UqXbqVg5xxfWBC4etXC5GQR7txhLy4UEjBNYGaG98MwiMSdnhYoKkp3wcl85uoe1ddLlJZKACIv31T9zfIyXXGuXdNx/Tr1hmtr+RpnSb+z03R1wXFSZTKrCG6l38lJDXv2EEGshsrOQyFxTyXtzPGglPoJH84HmGszcQMiuPHz7qZkslVwST7gi7IXmp0F5uZoHpoP/LPZZ24GR8+nf8lSJQ/P0dF1mGYRbt7U7QW71bKQOrh//3sDxcUs4d65Q85kKMSsrWbLzSgAACAASURBVLmZnMyxMQ2trdm9n0zpvHPnDHR3k+NVUCAxPq5B1yVqaqSNJsx3EFy7pmFtjZ6bykGkpIR///zzpus9DgTYF56bA3p6DLS2Uqi8qSklMC+EwKuvbqCjIzcfr7ubJdiREQ0+3zq2b/dhYSFbMSnzPubr2Q0Nsf9UWsrSc0kJhdqjUYFHHiHQaGAgBU5yPkfne/X0aHjqKQs7dnB+lJZmqyqpa+voYNn0859P4OmnzSzfwM3K/pnzQ11bXZ2FkRH3deiGAA6FCB6KRJiJb2ysIxDwQdcZgLa3m5id1VFWxuc0PEyKw9GjJvbutXKu01zl77Nn9Zw9vLNniRMgSIsCF4mExLVrBurq+P5bsb+6fFnfMj+V9zB3MDE3Jz4WJ5EHB+MnfDgfYL7NJHPjdePnbUVcV427ARtkIgydn63shV55JY5jx/LzGzf7zM0ECbq7DXzqU5ZrpOmUdwuHV3HrVglOnTIgJQEwFRUyC+mZeb+cEfSdOzpGRzWEwxoCAQs+n8DCApIkb/ZV19YESkqQt/dTUkKgTzhMknd5uURnpwUppd3f3OwgWF4m7w4QOHLEQmsrQTYFBcgiXzs3r0CA2VJjI51Ann02gWiUvaqDBy28+GI861BU16wk1s6doyvLrl0WLGsNU1N+rK/znmdq8zpHvp6dlMwQlWWXZTEzOXDAwsmTiays5MwZDxobszOMoSEii52AJaea092uCTckrXNuvP66JwuFPTKio7MzkcVzBJAjyDUxMsLKgWEAS0sbmJz0Q9MYCFqWwPPPp56TlMDhw6Z98ORap5mZ6OXLun1vq6tlTuGMycl0UfnSUiAep+B/5j3M9UwznwOQvxqTLztXgcO9gofUeNBj/Hc0cvUrcvUNP4rFz91+1sfx2Zt9ptMmydmTME1u8sePJ+D1pv+96gOp3k08LtDTE0BtrepbAabJbLaigu7qhw9brn1FZ+8rGLQQjWrY2AAGB2ku6/HAlh9jxsMe5Wa9H8sCjh2zbNsvAGht5fdW/UlnT+XsWQNPPpmA6gfu2CHx4YcaZmaQLMuyhC0EOZLOXkw4nNpAlAXS8jJ1OvPZbWWOhgYGE1/4Qhz9/VQtSSSEne1+8Yt8FuGw5vr3+Xp29Nxzt+zK7MGVlABVVSZ6ezVUVZlp7/XQQ1by81M2VB6PwFe/+vH2o1TAdPs2JfWE4Fw5dMhEcbGFqSkt676eOmVkfQ+Ar/30p+P47ncLEI0KTE15kyhd9iXPnjVw8mTCfr8f/tCTtmYWFgQGBzWMj3PNqL6t834PDWnJfjKzcfXZmTZhVVUSly4J1Nam3n9tjcFUc7PEV76Sbt2U65lmPoexMRoQNzdbOHXKyLKcyrRDc/bTVY8x01rs6NH7ayN1t+NBxnifR3op9d6h1nfruvBxf9bWyrebf2Y4TP5ZNEpe4RNPpCgaVVW5I03FG+vp0bG+HkV5eSF8PqIeKytZPk0kYEt0ufVqnBE0hQkkTFMgHGbpb/t2C4mEhnicJd7iYoofuPX5tvq9p6e1rCx6bg6Yn9dsr0y3TCgX+bqnh1q3XV06Ll8mbaCwUNqlurtRGnnvPR319ezdhUJATw9QUuJBdbWFQ4esvFWGfFlBXZ3E0FDKsss0gb4+9gyvXaMUoTM7DARS3Fnnez3/fMKV67jVQzHfPHb+7q23mMHPzWk2CjsSEbhxg9Ztqj+YOZdyZczHj7P3zP5vGHV1hRCCc8rv5yGjKgHDw8LmKWYKWZSWpjJBJw5hM6lI5zPKJSpfVye3/Eydz2Grij+5svOtlrQ3Gw9KqZ/woR6gG2DCrf/kNtx6d++9R+i03+/ez7t3sMHWLIWcHMTN1EycSjP5epD5Fo06fIaGdOh6FH5/ISwLqKjg3w4M0FMwc8FKKW2y/fXrGq5c0RGJsE9VXEyifVubhdZWC5rGzCsW42EZDmv41KfY+8l3z/LdazciuNtBoHRtVZCg1HvU3y0sCFy5QmeDSISatppGBR5dJyzeyTHNfKbuFle8p2VljPLHx9fQ2OhFWZm0N6pc/Z98z8r5u8yNdGKCMm+VlQRILSwI9PRoWFuTWF/n96qtlWnvdbeWRPnmsZQyS7hCeZeq0uT6OhVy1tZIfXELOjYLAgMBiVOnPND1dRQX++xDads2iYsXUwAz0wQuXjRQVCQxNqalCVk4Re+V9dXCgpZXKtLJoQyFaFw9MKBnicq77QObPdO7UfzJN+7mmeaauw8Oxk/4WFxcxPp6pStgoqFBbknJxq1319tL/tzOne69iVzyV4oekCuSfv11jy3kvLhI2kJhYbqlkBsH8dw5AzMzLOuNj2dvcLm+R+aiyrVo1OETCgGRyDoMo9AmfiuQy8MPm2kL1mlibFnMAIaHNXi9SEpc8VBpa7OwezeRplIKzMxoaGsz8cwzJoJB9/6Nc2zlQFeo1d5e+mq2trIvqPidug5bls8J4lBApcuXdQwPq0PMQl+fjsJCwO9nP3jvXnelkXzBSGYgc/t2DLFYIQ4etBz6sk4lo0xgCsvWbhtcro20uJigpnCYweH775Pv9sQTvB5AZPVk74Xn5jbX1HzIFK7wetnr0zTK7Y2PEznq9UoUFlIeMTPoyDR7VgHi0aMJ1Nfzc5eXBUZHY7Asn/3+Fy9SW7StTSZF8/le09MCExNampAFkK1Es3OnhX37LFepyEwO5eoqMD+v44UX4qitTReVz7UPuPlhOse98kHvtgqVq+er5u5W7bzudTw4GO/zWFxcxI0b1a6AidJS5LQmcg63yXj7NuWkduxI/b1yJ+jv1/CjHxVgbQ3Yv59qFUo1JxzOnxH+6EcFCARSTg6jo1ys165ptqVQpiVOaSkl7cJh4OGHLZSWUlFnZUXgt7818OabBqanNXszvVc5q5oawt2vXZMIBj04cMBCQYFM09h0vrdTJKGnR4ffT17Z5KQGj4e8v5ISCw0NqYOMhz1LgJlKLZtpUW4Ftarr3DCDQYJlcjmQKBCHUxJveprPsKKCPdCCAmDfPgtrazxo3Q6xH/ygADdv8rCPRBjorK8D77yjY2GBWVIiIRCJCHi9y6ioCNi0iEwE5r0o4rjp9JaXS4yPC8zMsNx9+LBlO6g4g5l7Ud7J9bnO+eAUrohEBEZHBSYndSwvM9C6fp1WXx4P/R5ra7NdWUpKACkl3nqLfd+qKomWFguzs6nMsqpKYng4gsZGP2ZnNUjJvnNjo4WZmRStSAG81NpRQhbA1iUUjx1LYGDAHVUai5HLm66gc2/P0y1THh1lMKHoMpmH3t1+lnp9f7/Iqby1FQPojzIegG/u85iZ8eDMGR0AN8PWVkaDdwOEcWuKezzphwDApvjwsIaZGcsVRECCLVxBA+p3VVUmhBA2kg2Q6O3loi5KSr6urIgk6Zqb/NAQ/72xwfJePM7sbGGBiFOApqsbG8w+6bxOb8WCAoldu7ZWgmlokHjttTh27BjH8nIb5uZEUoWDjf58IgnqmrdvBzweM6mhyv6Ok1z8UUFLbtdcXS2xsGBhY4NAlCeesODxyLzPQ4E4urp0jI9TEm7vXhMeD6+jqcnCjRs6FhdTVl0KxKA2lnic5rt+PzPTggILY2P8zIICiSNHLBv88OlPxxGNLsDvr3AFTeQCmyjS9unTBs6d05KbFnvHJ08mXOeu1ytx4oRp32snaGlmRkMiIV0/527AYPlEM5RwhWkyS0skJNraeD0XLpDacPCgiaYmpIktFBYSdKPI8gsLAo8/nl6SDIWkfa0NDRLHjy/j97+vRjzOebB3r4mCApbsMwUPsoUscgNT3MBxb7659bnrBoLayn3OvMaxMZpEHzmSyEnYv9vPSgHtdASDKrjh/Tp0yMTsrEBHR85L/FjGg4PxPg5G7UEUFPAQW1/nIjt0yITHIzdV3VDDbcFUViLJvYP9s95e9nKUzqTbhAKQd/Hs2SPR1UU+YGEhklGuhuPHE/ZGU1pKdRaAfYuVFZKGS0r4vjQHBnRd2CALIXitly4Z8HhIrSgoIL9tbk5gYkJsuQHvriaUukexmEBvr4bBQXL5FhZE2jXX1/NQCIVSziBq3KtCSr7hhlqlwW7+5+Hc/FZXGXBcvsxnYxjAjh0mLEugpMSyAwTnIdbXp6G0lNB8QGJlhSCjWEzi4EEeSM5NqqMjNxp5YEBDKESCdmkpA7yyMon+fg19fRq6u2lYXVBAPulbb/E7PPNMAl1dBtw2+82CGTViMYELF1JC1kqNxU11Rw23NWMYzACLiynkPTUlIKVMemkK/MEfmLhxQ6C8XMDjEVhbk0nQisSlSwZ27TLTUMKZ6GLns1OjpiaOlhbON/aE+Qx9Ps59Z0CTH825+cicu3Ts4VrMRI9uNQBUyjn9/RpWVpi9lpezHxyNUhLxyJEEWlqkjZKemREYG/PY6OG7DTbV60tK5KbKW/drPCil3seh/BhbWopsZ3rDQDLTya8g4xxupZNnn01g//70n5kmof8kzwv781ZWuNmwn4GcoAHltlBTw0NsZUVA01jqOnkykZODuLSUsqPx+3lAz86ybKd6JYbBjT0eJwpSqZkcPMgN9m6a905AkxNo0N5uYnJSw9mzBvx+C52dRBpSTNzC+Hi2jU/mM/ioWpRuIx9QI9/zyOxnZQouPP64iT/+YyrwuNmM9fWxXDc/L2wU4/o6OZNPPpnqI6pydlPTXE6LpF//2oNYjD1OVWL3ePje4TBRtj4fv4+u0zfQ6yVvT9fh2uvezG4M4ObuFGnfCvAM2JpwxbVrWrJPzxJ4RQWNj3Vd4NAh014DJSXSpi3kQxe7PbvFxUWsrVWm+Y0Gg3yGpoksHV8FnAuFhK0Us5V+HDVYNdy4ocPj4fpS/Vt3vdLNEeSq8rC0BAwOsuKzvKwk+lIavm42YXNzApGIZrc/7gYhr66ttDS38taDHuMneCg/xtraYrvcFY0KxOPAa6/dHRfLrYelfqacDK5e1ZJ2S5QTA7InVD7kqPpdYSHNhquqJIqKhL2R5TJFzrSjmZigxFVtrWWXX9fWYAtCP/10Ss1kM1Fp51AbwNmzwOBgCc6fT98cR0YIY+/oMNHRQZ6eEhNfXCSyz83IOfM+54OTOw9jN2Su2wa2GbVhs4PYeU1bEVxQG0s4TLJ9MMjsREqWrhsbTRw8mL2Z5+rbnD1roKCAwQuQLllXU8P3XlqivJ4QsA9hISik0NFhujrE5Du81P0gfSEl0r4V4FmuNZMpXFFYKHH4sInOzlSQMDYmYBjA7t2W7TFaUiIxO6tlqb/kopk4n93i4iKamsqzvDCLigRefTWOw4ezhQbupR+npOI8Hom+Ph1DQxrKyqy0/q26R5yP7gh5p3emorHMzoqkkD8/MxYTqK+3ku8FV5uw0lKJhga+prPTvKtgM1cg6FTeut+oVLG8vPz/j3JrnvGjH/0I//Iv/4Lu7m6EQiFcu3YNzc3Naa/Zv38/xsbG0n72V3/1V/jmN79p/3tsbAzf+MY38O6778Ln8+HFF1/Et7/9bRQoC4mPcZw6ZWBwcBo7d9baP1Plu60SsXMNZ4ljZETD7t0m/H4KYwNUMpma4oQ6fjyBkyfTN/dcZah8v9vK9VDLFLh9W2B+XkvrRba28hBXQuK57onbNQApIeNQaBKDg41YWdHwxBNOAW/aEp04YbqWLTMJzW7Xnu9+aBrfp6mJ5Vp1rx97zLTJ8LnEkD/ue77Z+6keY1+fDk2TkJJUlnCYB9a2bemlTfYY+9He3p71WYqEvrQkMDjILKa4mM+wokLiN78xMDbG8mBVFaDrElICpsmS65NPpg6rrcx/53fr7tbQ2Wmllc9On9YhpcCzz6beY7NnnOtz1JxS92JsjP30pqb0++PxuM/b9XWRJXrvfHYDAwNob2/f8jM+dcrA6iryro+tvD7XOujvJ1ezuJhzuKdH2HvEvn30zlT34ze/0RGLkXfMaoC00aHPPJPA7KzAc88l8NvfetDVxSBpfZ3920OHTJSVSfuZuH3/6WmBX/7SwOSkhvp6C5//fMLWt93sfqn7er/GJ6bHGI1G8cwzz+Azn/kM/vZv/zbn6/76r/8af/qnf2r/O+BgEpumiZdffhllZWX4zW9+g6WlJXzta1+DlBJ///d//7Ffc2enie5uI60P+HGoPDgXdCgEaBrVXA4dMvHEE+yTjIwIPPOMic7O7I1aKVCoiacAFOwv3ZvaTubfTUzQH/DqVSJGH32UYAwAWb1A1cOcmBD27zPV9z0eaTfwIxGWZYPBFIAB4D2WEnfVI3Tey0zwQOa1vPuuhpUVImuHh93dQXIBCtT9UQv+zTeNjAXv/jzu9prVM1S9qrU1K9kbYobf2clrc+tjDQy4P1vVu1KuJUDqUJibE/B6JQIBC6GQhkiEmUVdnQVAYM+euwcyZTqyrK6m/94NeHYvvSe3nt6Xv8znnnl/ALgCY5yBUOrZpjbzmRkPbt1KAXaeey4/Pete+3GZr3dbB2NjApcuEX9QUyPsoCUUYjY+NaWlgWT8fuIF1tbYPonHgVu3NHR0WPb9VvdwbMyDmRley549LEuHQqlnkrk/XL6s4R/+wYtgkIpIS0sC//APXvzFX8Rw6JD1kVS/Po7xiTkY//zP/xwA0NXVlfd1xcXFqKmpcf3dmTNn0Nvbi+vXr6OxsREA8K1vfQtf//rX8Xd/93coudcmUo6hUGnLy5X31EzPNZwor3BYmbTykPjUp8y8dkvA5hvrxzEUgvS117J/9+lPx3H6tIGzZw1UVZl48knKj2UegEAKHKIiYDVKSggAcNo75ZIRcwYjmZEofRhzo3Sdv3MexpnIXCB9A9ss63Xe987OhB2pb+V5bAXlt7kd2NY3nVxoSY+HUm+1tRJXrwr09gKLi+xtq2eVS+Lvo3y2G/DsXgPOXPfJ7Wf5gDFua+qnP/Vgfr4cnZ1wDboyQS3t7QTo3E1gdzcybhcuGPB4yCl1AgFVZgdkA8EMg2XYjQ2WyaVM9ZirqyV++EOPbRPmzDZDoezXOLO+X/7SQDBI6hGA5P9b+OUvDRw6tLGFJ3d/xyfmYNzq+P73v4/vfe97aGhowB/+4R/i61//ul0mvXjxIjo6OuxDEQBOnDiBWCyGq1ev4vjx4x/79WzFj/FuhzNKVMgthdoCNt987hWqrca9llvVUDqdzz2XKbWWfQAC7hHwjh0S773HUq3SF1URvPqObr6Hm+mWqs9z2yich3EmMhdI3fdcgYfbob+0JPG97xWkRfEVFRJLSxI/+IHH7vXeC6Lw4xj5PADffNNAURErFidOSJw4YdklzddeS9FGthKkqJKf6lmr73s3Wd399vPLF2y4ran5eWB52ZO1zk6fNmwg2sgIHV2Un2Ykwl6wKtdnVlQyv2OuoCVzHSj06Ows+7OKijU0pKGjw7T3C+casyzKx62sCPj90jYZj8Vg6/iqz+zqMtDZmcDUlGb3JDNf4wz2Jic1bNuW/l3KyjjH/n8Y/64Oxj/7sz/DgQMHUF5ejitXruCb3/wmRkZG8P3vfx8AMDs7i6qqqrS/qaiogK7rmJ2dzfm+A7lqTFscm/39zIwHPT0BLC0ZKCtLYM+eVdTU5I5+4/Egbt8WSRNUHcPDAQjBTbe/fw2rqwaOH1/GwID7e9y8WYny8jgikdTPLAsYHvago2N+02s9ezaIQCABv9/C4KCG7m5+Xr5rznUN4+MGxsa8mJ31IBrVk/+/ivb2NQSD3IQiEQ11dRoGB7Xk5wILC1MoLfWirCyOGzeEfd+iUV5DRwdsrlM0CgwMAG+/HcTamoAQlv3dDaMY778PfOpTYfvaIpGUSLO6zwBgml5cuFAKTZNoaVnH4iIBAg89FEZ/v8TqqoE9eyL4znfKsbSko6IigW3bYggGE1hd1XDtWjEee2zF/uzlZQM3bgQwOupDVVUE09MCw8M6tm1bQ1+fHwsLBRgfX4fHI3HuXByf+9wiAKCrqzrt/QFgYKAQGxsC8Xh40/mTbzjnqvNZFxdb0HUNq6sGgsFlRKPxtHnovHexmIYf/MDE0hLVZxYXASnTn9G5c6n33tgQuHq1GADQ2RlBKGRlzSn1PGdmPPj1r7PXinrGbuNu19e9DLc1NTtbAiEEJicn7Z9ZFvDBB6U4eDCMoaFCxOMafD4egn19Etu3ryEW0zAyAly6VIKysjh27YpieVnif/wP93VWWVmIM2fKMTvrQXV1HM88s4hodC3tvs3OVsIw4vZ+UVgok7xWA4axhuPHlwEA3d1BmCYwP+/BzZt+WJbA0aMraG7mPItENAwO+uHxRBGJpNbR6qqGy5cl9uxZxeRkAOfPFydpalzHy8sGBgYK8f77Ao8+GoZlFWF4GAgGU3NneVlDUZHEwMDElu75R9mXN+tP/psejN/+9rfx3e9+N+9rfvWrX+HYsWNber+/+Iu/sP973759KC4uxle+8hV861vfQnl5OQAat7qNXD8HNr+J+cZmTeKJCYG+Pg9KS6nAEokQZr99e+4I2O9nJF5URDNhv1+gr09HS4uFHTtKk9F20PVvAWDvXveGfV0d0N5elvV6Z2Q/PEyjXidEPRQClpcr7yoz3rvXwPg4r3tujpwyXWfZJhr14vZtaYNapKRhL8AI+ObNOezdW4UXX8wGsAwM5M5iz53zoKEhnVD+xBMC77xjoKio2I5spdTSeoxFRVbSdkhHYyMzmvV1H8rLqWlZWhpAVZVEXZ2Frq5qmKZAeztLTuPjAjU1JmprJYaHdZSUBOz7PjFBAnNLi4DfX4DKSh7iN28GsbrK6LylxYu1NRoRX7pUiaIiYPdugmqEkOjtJcrZ7weeesqE11u06fxxe66aBszMzKCqqsa+f8vLOnbsyJ4n6lk756G6d4uLWhLBmJrP6b04Bqa3bhn2e585o8M0NaytCVy7VoLnn+f9ypxT97JW7uVv8t2nXDzK2lqRBc6prtawvLyC+vr6tPtXWqqjrS2A0VEdXi8VgFZXSfp/+OEETJNzrLkZKCnxASjOuvfOa5uf9+CJJ9QzKMT8fBB+f/r3U2u+sZGVicFBgdlZGoS/9poPDQ1BTEwIXLrkwRtvGCgoAHbssJLoYx/a2lJrsaUFaGsrcQX29PXx2ZeV6UnbtDIUFJgYH6cZN11rilBfr6G/X0++lopHUmp47bVY1n7pdv9zAcU+rvFvejB+7Wtfwxe/+MW8r3GWPe92HDp0CAAwNDSE8vJyVFdX48KFC2mvWVhYgGmaWZnk/RxuB01JCXISZDOHs7zU30/n+eZmmh1vpax5NwobqhyWSEjMzGj44AMdgQDwzDNxtLXxc+6ljNfZaeLnP/dhYoI+dV6vRCIhYBgEcpSWSly9quGZZ8y0MllDQwIdHfNpB/hWe6a5VFiOH08gEIBrWU7d5wsXNJSWWnjiiRQAZWSEJSoFY79xQ09eg+Zarsrs+8zMkBpw5IiFoSES930+mtfW1VHKT0HgpZQ4e1bHyy+z/FxcbGJwUGBwUEdhoYVnn02hc7dSFnfeM10nmjkUKsLzzxN+ryyuKipoiaXKxy0tFiYnha0A4/GkyN5VVTILdZypkKPm7LlzOqqq+LrLl4loVLSSy5f15OaXfs2btQDcNlCn1Zj6DgUFVs4ydb77pObWT37isZG96mfKh9KJZvV4gMVFA6dOGaiuJlXEMKi9GomQutTbq6G4mDQLKSU++EBHRwdl6bailuW8J277h3qNE72+bZvErl0SDQ2pgEV9z+lprk2lDrRvn4WJCaStRTdhhkgEWF4G6usVFYdSdIWFEhcv6nbZVNlkUQ+WB+LYGFGp//E/xmxUar77/9vferBrlwf38Vz8tz0YKyoq7isX5fr16wBgg3GOHDmC7373u5iYmEBDQwMA4O2334bX68VDDz10367DOTIf9KVLRDrG4yaGhkg+rqmRmJkRNjBjakrL6ucpJOP0NCeVWx3f+ZmZG8ZWFTacG0thIXUjl5aAt9/2oKwsjooKuSVAhds1aJqFkhIqppSUAGVlBB/Mzen43Oc28gKIMq9xKz3TfP2YXBuj6itlypctLDDbjcdT0mqqX7ljh7QVanw+HoD19dl9n8pKAldaWih6MDjI7MHnowtCJhZsbS0ly6cQoisrFoTgf7v5M+aihDhBRx9+SIStEGYawranhxtqWRkPrbU14K23qPlaX5/aqJz38Ic/9NjXqEZREbOJ6WnN7qtJCUxOapiZYY98fZ3qSKWl1MPs7SUVxznUM1Dfc3JSJDN8qhspKo1zA52YAJaWKIsXDJJWceuWB/X1Fo4cSWy6Zt56S4fPB9TXC9y6xQBhbAxJebfUfNu2zcL6urADLMX727cvAssKYHZWYHFRx6uvbqC2lkAz9qiZRVmWQGMjA4MbNzRUVlpbUsty3pPLl9P3j5/+1GNTTzo6rOR9JVJZ9YkzZdsyZdiWlgSOHTMz1qL7OiotTc1PtQZ8PnInGxtNrK8L7Nlj2XOitFTir/4qf0k719ru6Qng6NG8f/qRxieG4D8zM4OhoSEMDAzgV7/6FZ555hmsrq6ioKAAhYWFuHjxIn7+85/D5/NhbW0Nb7/9Nv7mb/4Gx44dw5/8yZ8AAFpaWvCrX/0KZ86cwd69e9HX14dvfOMbeOmll/AHf/AH9+W6M4momar/yr19YEBDRYVMI8hubEj85CcFSf1I6noODem24PcPfuBJyq8RfOO0qclUr8h2GrdyOiM4R6YLuMdDVY71df67uFhmWUu5+Ua6XcPEhIaGBjrV+3wkjicSJBu3tyulHpn1fpmqF1tV/N+MvJ9vZCqF3LxJYe+KCtIgnEoou3dbtqADD0CJL32Jm1Am6XxoiGT20lJG0+XldPuYm6PAt2FwPiwvC9TVWVlO6hMTVCeizJluE+ydVklu9//MGQ8aGymU3turJ+H4UWxs+NHaSiL29esaNI1zi0bGzGarqiTq60luHxoiMCcUYiaUS1FlbIzi2QSbUAxifl5gOVVsOQAAIABJREFUYUFDVZWF2VkNuq7AS5pNC1BZpXoG4+MC16/rWF1Vvp68LlXGpaJOivT//vs6iop4f4Xg/YrHgYICgQMHUp6T/f267ZHY3a3b4hGTkyzxnj9voLCQz3t0VMP8vIa2tnT1oEhE4OWXKdY9Ps4+tc+3gt27i7Bzp0R1NUvyykLqwgUdPh/nXlkZf+/xEGh26JDcklqWut9uBHtFqVFCCMEgjZhrayU+97lEmo3chQs6qqr4t7lUs5wOODU1Fvr7dXzwAe+bEuuwrHSFn6EhuvxEIiI5d+knefUqlXTc9h7nHqKuy+nf6fEAo6PrOHq0cNN1e6/jEwO++ad/+id85zvfsf+tSrD/7b/9N/zRH/0RCgoK8Prrr+M73/kONjY2sG3bNrz66qv4y7/8S/tvdF3HP//zP+Mb3/gGXnjhhTSC/7/WyEQUptzbqToRjbKEUVcnceYMZbhqa1nrv3WLKiIK1TY/L2xSrRv0GgBOnzYwOCiwsWHYGpdKUDyTd+jWRxkeJpG9tlYmS74S9fWUgRsfF2hpYTntzTeNNAL8ZmhMFW1SYoq9uI0NIt6am1lybGtLbKmMcjf6pvfKj8rMNlUZ1OlusmePxDvv6AiF+Bwyy1Xu9zmF5HNy5gj154Ho8Ujs2CHx9NPZuqPl5dz0bt7U7E12fV3g0KH8QuVVVSZ6ezVUVZk2wjYWY0lT3b9AgN/7zh1K/pWUUM2Emq3MToJBmeZMnysrV9nE1BRNqtfWRLJ8LpPAHKoS3bnD7/Hwwwn4fDItm+vsNPH733uhaRLLyzSALihg9jo5qSUDjXReK8BsLBql7unKCsXDFxclTp/mmigtJdjnuefiWXzVkhKJnh7Ksq2scO3SGBppn5U539Q6d4JxnKXQhgYKqWf2+k+dMlBVxdLpoUOmLaiQKXafOS9nZhjADA0RNb1vn5mUk0ufx+oaMitXBQUs4+7ZY2FoiBUHKWG717i1WeJxgUceMTPEEVKI2g8/1HH9uo729gR0ndn2qVMeNDdbKCyEnTlnrg+363IKeUQiQFnZ/eU4fmKUbz6pIxN846ZUMTwscO6cjmCQPZodO9iXOn+e2pIqUotGGY2trACPPGKir09DLMZeVjTKrKujw7TVOPr7Nbz5JrNDynHx0O3sNLGwINDebmUpuqhJPjrKLDEQkPjd7wyYJuDxMOrXdWDnTirtxOPC5i6pDWWrajS3bmmwLMLal5bYK9U0ajCePJlAV5fuChJaWZnCV7+a6gm7KZhsViJ1G5vRUDJ7w6oM6ry2fEood3OduagMbrQGAPjudwuSouCcPxUVMkuo3Hn/5+YIOnruubit4hMKhfD88wFbxcdN7eWddzQMDWloa5N2thSNApYlcfSohc99Lnevb3xc4He/M6Dr3KDDYc7HykoeuD4f+1QAlVMUSdyp+vK97xUgFKK5b2WlRG2theJi9sB27qSDiRKSCIWAnh4NtbUMFkMhZi/097Owdy/XRHe3jpYWE5/9LO/l6dMEjhQWAq2tFn78YwOlpbTn2rnTxPy8Bsti5v6ZzyRcn6Na55HIpA2+yZwfbuvu/fcpVp45r/KpBU1MCPzX/1qA69d1lJdLNDUx8+zu1rB9u8RnPpPIei8AaWtrYUHgvfe4B+3ebWUp4kxNaWmcy+VluILw1tc536gTLe2Mf25OYGmJIKPKSvrQuj3fzP3ReV1Hj5r2vd61qx9Hj7a43o+PY3xiMsZ/L8MtovZ4BP7Lf9lII8jOzNCpoLQ0lZEUFgLT09S7LCqCay+rsFCz+UOhEG2ehoc1+P0WioslAIkPP9Shaak+kTrQfD6Z1Uf57GctnDhh4swZ3UZHPv54Iml/lJ4JZqrRLCwIDA5q6O2lGezevZbdC1Oq/w89ZKGqCrAsM+sgyWWjMzxsbDnzctoE3Q3IYmxM4Pe/96K5OWUaqw6j555LKfiEQjLvITcxkQKqOIFWQH7+qFMpZyuHqVv24cxilFqQ0+5r/34TgQAPtkcfNTEzE4FpBmynDvUdM8n1IyN0pZASjmCLQZbz2tMHs73KSguLi5rdG2xsJPdx1y4L587paGiw0NaWuueZgJOdOy2srgKaZtrl/GgU2L7dtAWunbxWRTxnJiywssKscfv21JXFYlw/ajj5qhUVtIoaHKQOrwJrhcNci7l69Gqdr65q9vWMjmoIhWAHtF6vhGGkwEuaBmzfbuHSJQOjoxZ277bsICWfeEFDA8X419YEysqkrWVbXs7+baYQQltbAj/+sQeZ4J7HHjPR1aXBNIGjRy10dqbmQCbnkvcRKCoy056VAmE991wcFy+m+pVVVRJLSxoee8y076vb882sqDmvy3mvFS3rfo1PTI/xkzoye4y56vMHDrAhrvpfGxs0NF5bY5hvGMDiImklnZ3cTMrKkNXLamxktlBSwgVYUcFeTjTKSRWPs+x29GjK7f72bZZAursNBIPsVzn7KI2NzIz8fi60I0csHDuWwO3bWlpvb35eIJHgZ5WWyqQANPsa4TBdLoRgeU+p/vt8/I4vvJBwEVR271etrKxiYqI0rWc2MqLj2LEEjh+n08RmxrqZvdCbN3VbBHxxkX0sIUi4vnNHw/g4NSZ7ezWcOuWBadKzb3JSS3uOysyZ159+DdeuaVhaIpgl09lC9ULzXVc+wezNhMrzmSUfP07EbHX1OE6cCKaJ1Kue7MCAhrExkbQi4zxIJPi5e/fSMDqXWwKfm8Dp0zRHjsXYw1IUl8ZGia99LZ4ErOQ36nUKTE9NaVhbAzY26JBRVAQ0N/OZmSZdPaanNYTDDKJiMQYHTU0mlpZ4YBkGhf3HxtjT9fmA8nLg6lV6Xc7Pa4jFKJx94kQCu3dLmCaQSGj40pfi9nzL7JOpe3fnzirW10sQDCIZcFJsvbiYvdHFRQ2NjXwOt27pqKjg/ZibA3p6DDQ0SFs4O9/o6tLR1CST60NASmZiS0vs2a+ussLR3k491JUVfnfT5FpXNnAdHRIvv5ywv5PCRKjesEJgLy+z37++nhIdUM9qbk6grAxZLj/T0wwGgkFk/U0KE5G95pULydGjBBtev65jcDCO2tqivK4jH2U8OBjv83BTgQ+HBW7d0tHaamHXLmlbwmQCYiYndZSVWVhf58EnhMCrr27gwAHL3gSdoI0vfSmedljNzxNCXl7OLM3nk9A0bpyPPGKlHWjDw2yQW5bA+Ljy1pO2XyGdxiX272e5zM26xucjiEgBbtbXAYCu6E1NPMCvXDHQ0OCu+p+5qeba7NfWIqiu9uc9LJwgp8VFgdu3ddy+zey1pETi3Xdzg1Fu3iSQoaSEfd3GRgumSUHrqiraJc3N0adydVVg797056gO31xAK7WZKCThxAQDinAYea9LjbsFFpWUMPgJh5HX7itzrmYK1W/fbqG1lV6Qw8M6Dhww7YMin1uCChDW1phx1tZyPj36qImaGvaud+60tmT5pb5nNCpgmgT9RKNcUzt2SJw8mcCOHRZu3WJAUVYG9PTw/j7+OA/wwkIGiYbBn+s6bZp8PoJtLEtifJxyZuvrAouLPBwti+XU2lqZBdpyA53x3k/awcbPfkaloECA88HjYZAyMSGSziR8VoEAg+LGRgulpcDhw5vbsU1M8KBvbeWeMDFB5G99vUwiQQncGRhgwFdZyZ5gLEYkdFeXhkhEw9GjCdTXp76X08KsuJjXrQBhui4QDhMs5nxWymYq0zZK9Ss7OsyknGX28801B9rbzbS1MTu7ipGRYE7XkY86HhyM93m4HYyZG6bbxu7cADwe4OBBCy++GEdHh8y7CToPK5+PE9OyGEkfPGiiqEhg+3Zpo8cAvu7sWR3RKCNZTQNiMUbiAC14tjKJTZMLvblZ4sYNHiLkK3EjbGoiou/5581NN3rnPXBmLEIAPT0SRUVeDA3p6O3Vk9QDogLVe6gFrXziVldZGuvrY6ZeXm6hudndW0+hNNfWGDRs28YNc31doKWF3zUaTR1yueyPMtGyPh83rHCYJa9Mv7xTpzyoqLDsTH6rnn/O+5VpTaY27N//3kBFBbBnj4X2dne7L+dcdWa7ExOsYNB2it9vaQm4c4clcreDwm2+q804F9Iyc17Pz2u4fRt4800PLl/WksCvFLK3qcnCyoqGXbvSA5ORES0ty759m/ZIkQgPz9FRZoe3b2tJXiPBSlLy3o6Oajh+3MSOHRLz8zwgKysldF2iri4byZzPKioeX8D6eiXOnjXwm98YiEaJCVBrLx5n5lhcjC0hq53DeRivrwtMTrIdMjDANTE1pcGyBDY2yCeMRoWdzQUCRL9eu6ZhY4NtmyNHTMzO6mmHjdPCTGV/LNMSJGdZvE7nHpTPP/Rzn4tD00ROVHiuvU0d6OqZxmIhFBcX3ZWP692MBz3Gf4OxVa3LfAjKXL9z9jDLyiR27jTR16fD6fIOpPeOPB4i7fx+RsSFhRKdnRaWlngAuGmOqv5eJsH7y1+O21qobj2v+nrrrkSS3fiaV68Cv/ylB3v3Es6/tkaC+qOPpnhvCqk6NMSeycQEy7i1tQQ+/N//68HCAhd2aSmzlu5uDaGQiZISypgB1C5dW4NNDge4MZSUkLYiZfozcz7HTLRsRYXErl2027l6lYIB+/alBAOUiEJzc+p7KKTryIiFmRn2WQwDePXVzYWWnf3JhgbLJs8rYEu+++4mVD83R/7qrl0mWlslZma4oW8mKpHigG6OtFTzejP3hcxrBLYmOK/QnjdupEr+zp6mZQE//7mBpiZpI28psMCMv7jYzOoJZ15HPE5lme9+twB1dRUQwoOmJgt79pi4eVNHTw8pPYZB3uKjj6aeR651kVJ20pIAJZYzFWE/EmGbZX1doL+fNJbGRgvV1Zyvly7puHKFwazPx37/0pLA7t28l14v2yWhkEz7fmo/qa6W6Ouj0waRpyYMQ7iKkBBBnEizlHrppQ0kEgLd3fqmLiPOvU1971/8wkBjo8SOHbn7zx/neJAx3ufhljFuxT37XkdmxFVXJ/Hii+ku725RGRGoNBeuqWH0DXDh/Pmfx9MyEGd0vLDAfpzXy41fgUtylUSeeiqBkRE96+e5ynBAdobd3x/D6mohEgmgupoR9/o6CdIPPZTei+rupsbq/Dw3jaYmC/E4MD5O1GVTk0xKt2nYs4ccr6kpgd5e0hz8foE7dwQmJwXicZaOTJNR/MICM5/mZgUoIj9rYYEcu/HxlKO6Kh2p3lQkQksmZ+a8tCQwN5ceAZsmASYDAzrCYW6ULS1WVmS/2X0rLCRVYnWVfdLRUQ0TE+mlM+dcdWa76t7NzrKnx953uhmtkzebWVJ0Orj7/dz4KivZz8pVJvzHf6S6TEUF0tR/hoc1PP20mXWNang8DIacfE9niX/bNmkbBR88aMLn47NXlQfT5PuVlkoMDel2+XBtjYdjKKTZ4DWVlf761+Tk9vbqGB4mB1Nx+mZmTCwv+9HQwGxzaYkthuVlDeXl7Cd+4Qv5DatVv3x5Gbh2TcfoqIbLlw2EQmwRTE8zKywrY+C3vs7nXVWFpEiAwO3bzPaPH7dw+zb7/aEQS7jr62wHuFURMkvXACsE27fnrhJMTAi8+64HjY0WDhxg8Pm733lQXMyMfzPzZef7qL0mFmNwOjXFfqhphgEUfyx7ptt4kDH+G4y7kWRzjq26WmyFq5f9GgNjY4S1K87atm1WlgJ+pgTVrVs0wg2FUlJiKgvIpa5TW+v+81zfL1PxZGjIj7IylsaWl8k1e+wxy1646vt1diZw6pQXi4tISmpJTE9rSVQjMDZGVR/DIB+urEwZqGqork5gZobw/pkZDU1N9Btkr0mm2R+NjAhbuzQWY6/w/Hkdjz1morDQdFUbqaoi8nV2VsPEBDPujQ2CPEZGhJ0FhMME69TVpQcOoRB5eE5qSF2dlQarv31bx86dlh1lt7ZaOHOGZeW2tgRqaiS6ugzU1mZH/c5sV6Gf5+cFKiosm2u7Z4+VZbXlhuwNBq2s7MZtvjuf/4cfGsneWOq6Mt0Xtmq5RB6olSzrpebc9LSws9KyMpksDxt4+eUNjI/rKCggR5PBgQYpybtsaLDsud7ZmcDwMNGa5eUSN28KRKMaCgstVFTQmaK8PGUJ9+STJm7fFsmgJF3CMdd6OXWKaPVLlwzMzlJoW0oGOkVFEoEAM+G+Ps6z0lJmt9EoZRYvXmT/UNOI5lWerd3dBpaXpY0kByxXdZ2tcn8z1YL27qX4xOyshmDQwuwsWxFOl5F8Js/OvaatLYXAHxzUEAxqkPKje9vmGg8yxvs83DLGe1FgydfHcCpYqCj9bhvSgQAj5IYGK9nHlEgksjM5Z5TuBKmEQgK7d5MOojIIt56X+v5uvbBc3y8UErbiiRBAJLKOeNwLjwd45hkT5eXkqykQi/r+V67oSfkzIgFLSpDMFgnM0PUUEMLvJxp3Y0NACImaGqCxkX2ZsjJmlhTpljYQ4z/9pzj277fwzju6beSrabz+wkIe3Hv3WmlqI+oehMPA668XYHUVmJtj6WtxkdHw1asGpqcZzQcCEufO6djYEPD5eCDdvKnj2jUd771noL6e6i1nzxr4n/+zANPTwMICs925OZF0SuD7Tk2xB7Vzp4W2NvajFSBp5870flh/fyrbralh8EDaD1BZKW3qjbPSkQl4+uADHTMzAjMz3MBHRqhuo0Ant29rOdWR+voIAisrS1VWFhf52SpjzFWVeP75RBrCOxgEnn02gWPHTHvOhcMC//iPBRgcFBgd1bCwQHGDfftMFBfDBpH09Ojw+8kL9HgAgPqhSmXq3DmK97P/ymcZi5GaU1gIzM+b8PkKkEiwLO8GYnPuC27rRa25t9/mgePzEeGZSDCYi0TYZ1VZ7YEDBBktLAjcuEHsQG0tPzsSEaivT/V7y8ooR5hIMKvWNOC553JXb3IN5/qdnORzpk0Z500wyD2itVXxm7lnNTVZrqjxzL1GKelEo6y4NTev4DOfKbwrnvLdjAcH430ebgcjALvcGAqxEU2VitwHWi7ATn+/jlu39Jy0hK2OrR7WzjKwE6SiUHhugAG38lrmteUDJHV2mvjFLzz276PRNYRCftTVMRMZHNTSQCzq+1+/rqO+nvBwOpELFBUBCwsslR06ZKG5mRu/aSKp9JEOgujt1e1FvW8fncXb2vg9jx8num54WMNDD7EsNjRE9J7Hw9JPays31YEBzSYrT0wQ5VldbeHOHV67z8feVGEhCdamyYy2upr6sZOTGt57z8CHH+pYW2NWX1AAm7S+tJQC9xA5SQ7rzIyW1DkVNrK5oUGip4dBhqbxoL140cDgoMTAQDH8fpa8PB5mIaYJbN8u8dnPJlBQINDQYCURhwJdXRR/6O/X8MtfenDtmo4rV3hwLy4SFCUlN+N4nJQiwxA2YlTN10zATFERaUXRKFBTw57v5KSG9nbqqE5MUJkm8wB0InHdDhk1H3/yEw8++ICAJEXe93iYmUQiwqawHD5soqBA4No13QaTAcD58wbOndNx/ryBYJD8w3gcGB1V31ti3z6J1dUoBgf9KC9n1urWOthsfag1d/MmuZSGQVEMy9JQUCBt9OnGBpWwTp5MYGiIoum1tWyLhMMESs3PU1ZvcZFSfIcP85oICmKf8dixdI1at5GPVjQ/z3YDgxXO7aUlVXrnfnL5MgOOjg534FrmXrOwwGdw4wbXS1VVBAcO+B/QNT6pw73HKPD66x786EcFSRWObKh/5sjVT/ngA93mzuXjuW1l5NtM1HBG6eGwwNISNxXVo8jmJeXnE6rX/OQnHkxM6BgZ0XDnDg+7cJhKGc89R/kyIXgQFBRE8eijXhgG+WbNzaYr/SMQ4OfV1XHB+3yMjDc22I8sLk6hI0dH+XmaxmwwmHTtWlgQWYs6H/dK9eMSCW7uDQ0So6PsBdXWpqLjM2c8aG+3EIkwig6HBbxewDR5bYOD7CGaJvtsly7pWFlhCZgOHDp277awvMwD1TQp/zUzw8xnbU3Y/bTKSmaHNTWWnd0Iwc/q66PAdmOjhQ8/1DE+7sPCgkAkopR9Utlufb1MQwnfvq1j1y4TwSDw1ltGUpaOWXhPDxGRfj9QVMTel64zi3/4YTNrvl6+rGPXLmnP7/Jy9iHv3NGQSPB1dXUsC7tp/lZXS5vjtlnVRGXE6+vkOvp8zGBV7y8WY7CjDt/Dhy2bY2lZIgmGYlZkmixbrq8DnZ18XlSHkaiuBmKxdfh8PhQXUwgiM+DcyvpQa45AOILJ1tYESkoovVZWRqHw5mbSaZQW69mzepKryaBI8ScXFgTGx3UcOcJWCfm37Hdfu6ZhclJLoqf5vDIP7MxrHh8X+Jd/8WBxkfOGQC0if1dX2dcfHNTtPSUcBrq7DXzqU1YWOt0ZQCqk7doa8P77OgYHmdFu22ZheBiYny90Fdv/OMaDg/E+Dzdu2G9/60F/P7MXIVhyqKkhMjTXgZYLsDM+rqVtKEB+mPdWsrd8r3FmlqurAktL5CXV1krXaDgzE4xE6FJw9qyext1bWSEIYHCQC6OqimWTpSUNBw6YiMeJINy9W8LvX0Rzc1ESbQgcOMANNJO6cfSo6cr3PHiQ/aZYjFnI5CSdLlpauIFfvGigqIjXbJrIWtRutJVz5wz09bEfOTioIR4HHn6YG2VXF2W+3GgYiiQ9M8NF7/USBTk/L5IoQSJffT5pl+lqa6Vt02Wa/J9yWJeSZadYTCS/L1VlVOluaIh8zmCQpcxIhAfy+LiGgQEDlZVEGvv9pC2wj5cOxti508LCggJqMbObmWH2EgqJJMmbB5qUArt3m/B6VRlb4OhRK2u+Dg1pyY0xRcEpLWX5+stfjuPGDZY73QTzAwG56eHinNe/+IWB8XENlZW8XnUN8/M8EB591ER9vcT4uMAvfuFBby/LpJOTBC3NzGj29dfUsHQ4NUXJs1iM37+2lgAlIaLYu9eHlRVmc4EA0tSPtkLdCoeFHTBOT7M8rmkCy8sMROibyCxQgalKShiwVlez711UxEN9ZUW1BrimFHDs/HkDv/udkfwbCxcv6vhf/8uLSISBmjNw7+pKUSeUGMbqKkFwJSV89q2tFkIhJIUwLJw4kUijaXg8DD4UtcTn45xXAaRlMWjr7SWPdn5ecyCIgXh8DabpQ0GBuC/gmwcH430eudw1xsZYcovFKOh786YGXWeG9Nhj2aWMXP2UhoZ0TiKQG+G61exts17m9ev0ZDx5MoEnnjBtfpRb+dWZ6SpCu2HA5j/90z8VYHaWm/ulSwSGSMnPLSuj9ms0Sn1X9f3X14lIC4fZ87p6VYfHw2h4fl7g3DkDKyvsN4ZCvLd37lB39vnnE2httex+aizGQ6eggKXS+nrysKanWQqqq5NZizrzO4bDqV7O+jqj5rk5Bg719TJp+poevDCr0tHeTsDMykpKPN40CaaIRpVLgoaKCpasg0EkQS+cQ7rO+VBRQTBGY6OJ5WVmvn4/uWaqV9zQwIyvt5elx9lZAV1nVre0JLC2ZgLQIQS5rgADrF27rKy55Hyuvb1U9AkGYSuuhMN0da+qkmhvl3kdQsJhksUvX+bzDQZZehscpIjAlSsGhoYEamqY1SmlltJSXt+dO8xgVRmztBRZQeblyxr++3/3or+fz4VgMa6fRIIHRiQC7N+fwJEjMk39SAjOA/a6NczNURS9okJicZEZdzzOoMU02eN77DFmbxsbIfT3lyAQkNizJxuRuZkzjFqPgQDVsYqKaHjd2Ghh2zYrWbXR0NDAZ+REK6s94/ZtDdXV7KNrGsvZ0SjRs4WFbMcMD8N2PblyxcDqKg/T9XUgEkkP3BUX0okzCAY5H+vqKABB4j/w6qtxHD7MdaWqUYGARHe3jqkpBjWqvzk+zudtGAJXruhJfiTXomWRL6yUdzY2opDSj6Ii5OR5fpTxAJX6rzwUwrKkhBnAxAQjbU2jMO/iIkEkbtygTHPi0lJGjjduGPB6ZZoZqkJr3a0pci5umHL0cDMDzueZ6EQOXrtGaHk0qtn2VbGYSFoGkTBfWkp05uSkhhMn4mhqIpLQ+f2Hhz2oqyMn8/Tp1BQOh0Wy9CYhhMT58wShPPaYaWtOZt7L8XGRpc/Z1ESu11e+4kS85V58XV06tm2zUFsrcPkyTVilJGI1HheuHDWluVlRIbG2ZkHXSfHw+y3s2cND8e23DcTjNAYWglE1M0MknT0oGL+6Cvj9Fj79aQtTUzxA6+pYJmtsJDo3ZfQs8dWvxvHb33owN8dNCWCPqb4+hkiEBsW8fqr7dHamcyYnJjiXLl3SUVPDuWsY0nb7UBtYb6+0BafzOYQo5O2RIwnMzvKQCgYldu5M2I4PbubP1dUW+vs1dHXxOrZtSznNKKPjiQmB06cN/PSnPFxaWyU0zcL4OOfGwoLA9u0WAgEBv195UTKDLSyk7ihR2izhTU4KFBVRWGBqilUfEvbJUyTfT8f27fx+AwOFEALYt08mRd45lxRXcDNnmMz1uLEB7NljoqyM86uigvPS50MWD1HN87Exum8oygsDKRo2nz9vJL04GQDNzLD/qyoW0ahAYSFdSw4dMm3ErLpm9awAYO9eVgbyuYGkrxdpc1pLSy0sLgpX/mhBAeDxWBgfT3GJYzEKPmzmA3uv40HGeJ9HdilVs+WSLlwgH0rXAQpyCztDcisPqChwdJQRo9fLbGl9nZnFygoP1hdeoEJOLq1OIAW+UNqGjAoJWMnXy4zHhasPX66hotalJWbLus7INJEgms7vJ1qyupqbtBBEg3IBEI2q0Kaq31NTM47HHitDSUm6RuStWzq8XmnL6TU20t4mHEYSDZfy3VNZb1WVdOWyKZmyrQwV9at7qvhg0SjBN3Nz/Ly+PpoIz88Dvb0GCgq4QZ08mcBLLyXw6KMEekQiBNYIwehb01imCwRkkoqQUhL50z+N47nnCIoxTWD3bonXXovjpZcSeOyx/DqeFy4YmJ/XUFBJiRwvAAAgAElEQVQAFBVZsKwEAgEj+W8ibA8fttLAGGpO+f3SlkpbWWFfdWmJfU0h+N07Oiw88gjL1rt3Uw+0oyPVp3Rm4Ldva6iv50He2iptAM2HH+o4cIDPcXSUpd+ZGQ1Xr7LH6fFI+7tTh5bSY3NzFEL49a89eP99HUtLPPhY0qQptmmSlxoM8nu2tVG7NxewLBRikOn1UhxACWAkEiyptrRQKWdsjNczNSVw/bpASYkHpqkCG66ZDz7QbTUlpVjjxuvNzCh7ezVbdzUWAwD+/Y0bGoaHmdHeukUHka4uHaGQwOHDJgCKx/v9KW7jkSMWVlaAhgbOByl5bwMB9lo9Hh7m0ajAwACfdVWVxBNPmGk4g/FxkVQTYgDR1GQlgUvu60d9JyU32dpK1LeS4VP80UiEn0vPTZEs31PveXY2jh07CvDss3ePoN3KeHAw3ueReTA65ZJmZijHtLJCC6jOTstWZslVHnD2JG7eZF+mpIRAj6efNm0z1Ez4/GamyIqorQArbr3Mqio244VA0tiWh8Hhw2ZOyojahN95h/wsr5cIvnCY/ZH1dW4UZWXShqF7vXT26Onh9TjRplJKvP++ie7uICYmFKKTi0tFnLSy4iadQodKV4h4X5+GK1cMGEZ6Ce/EiXTNSDXyEdgzCeGBAAE+Fy8aOHDAhJTsq1y/bmD/fhN79lhppbWGhlS5iVQFkuB37bKwYwfLkYuLAk8+aeGVV+I4dkxpTirTXpHVw8o11Jyoq7Pg97P/tLQUR2Ojgd27KR8Yj5M4/rOfGXjjDQMzM7ScCgRk8nBRPo5AebmFI0eoapRIUN/zi19MUSQCAWYy773Hzbqz00wT4M7soauy++govxd9EBncse9KYNDkJJHHkYhISoURETo8zCDE55NYXdWwsQHbnzAeh83PfeWVOL7+9XjS5SU/sOzyZR2lpZbNmZ2a0hCPM4N65BEraRpOhGhtLd/L719FTY3XFuuWkvdBlVYti9fl9fIgyAbnpN+X+XmRFIDn9yB3kVUXr5ei6eQwptbCyIiOzs4ErlwhGEcJv1dUSFtU4sABKxl4wPbKXFnhfQNYmqayDg/N5WUeiJGIQH+/nhSD33z9uH0ntcdUVgJra3QgGR9nOXVujuX44mKZ1FtlYN3WtohXXvE+oGt8Ukcud42FBZZMKystnDxp5kR1Zo7M3o4bNUAdrLm0OoeHNbS2pvwZ9+617H5NW5uFU6c8dnaTSFCtpaGBklAeD+yoM5FgCS8c1vJSRhSlYd8+C6ur3ARUP259neAMao+SQN7WZmF4mDJ2J0+aNtp0aQl46y0PiovD2LatCKurSPbnuAE6NzPlR6gcIBoapCtE/M4dDYbBhn4oRJSiEBI3b+ppnEggd/+1vd3EyAgzaKV9ubbG/uLp07oNSNmxg9D64mJmOY2NuUXUM5+d0potKgJefjmx6TVtha7j5K7u32/BMBaRSBSjpoYBxdSUSBLqefBeuEAqRmOjlex5EgCSSPAi//N/Zqb6wgukOuS7xnPn2Pvq6tKT6E/LVkQKhYStI3v4MIOEsTHNlnCrrOQ93LYtla1u386AkObdPOwBqr8otPHqKuezYVAgXwiBF1+M5wSWjY1pNkGd5WMNjz9O9PP27RTnVr1cTYMtmr9vH8t+AGCaa9D1Qlsf9upV9sUPHyYik5rG7HNWVkqbuqUQoU4+KQE1PHg6OkwsLgpMTzNTr6pi4EAyPTMwhXgGCMBpbWW7pbU15e6iHD5aWhgYLC8T6NPebqGsjP6W6+sM3NvayEUNhwksKy2VGBnRbfsylkQZ3Gkag3NnINndTRrP+LiGCxcMDA9rSfQvA7AvfIH805ERDW+8wWC1uZlrYnlZw1NPmTh2zMTXvx5HdfU4WlrK80/wjzAeHIz3eeQi+O/caWHfPguLi6ynb1UebTNqgPNgzYzM/H72glS5sLQ0nahtmgITExTYlpIHpSrNHjjAAzMQkLaQ8Pw8D8q332aPs6IC9mJ34yRZFktOoRAj+6UlHuaPPmol0aQSJ0+aaGoi9P7ZZ3koqtHfT1m05uYVlJQU25uK18vStBMl29goMTDAjHL/fkb4bhDx/n6CgY4ds2xXgkCA4KDqaunqlpFZTrYsgeefTyeEb9tGvt3kpLB7jqOjdC2prk4FMgsLAgMDOt55h/J1XV26bdezFVBVrmsaGaFSTT70sfMgGBjQMDmZQEuLFzt3WkneowbTZKlOBURLSwKTk5wjqnSs64CuSywuusvUuSGTu7v5LBUgRWU1k5MafvELAzMznNfRKOcFuZI6duzgulEygMXFBH00NDCrqK3lPKRXIkuKwaDE6iqRu/Qd5X189dUNdHSkaBNOYNm+fSYiEV4DDaJ5eFVVSZvKo9aTZXHeOEXzUyXPGB5/3INQiN9lYkLDpz/Nea1kBJ1iDQoN+/rrBSguZl82k0+qwGDXrrFXx2BT2HPF62XQlRksHz1q2ujp/n66uug68MQTNAW/eZO92i9/OY7WVorpt7dbeOopBu6Dg6RgxOMUY/d6KcTg87Es6yyBz81x/irA09ISy7137jBQ7O0ln9LnA4qLuSb372fVZH5eYHERKC9nC8XvR1LIgoC4zk4rJz/84xoPDsb7PDIdC5xluFwEZQA56RJOdGpxMbI2f+fB6oZkTSQ0/If/EAeg2URtdSBTCSal+lJRwfLru++S9zU3J7CwQCqCYTBCVr20igppK124aS46S8gtLSzdlpcD7e3cWBRQRHEIq6ulfeip0dVFknVpaQjFxcUA+DmRiMDLL7OnduAAs89IRGzpPScmKFvV0iJthJ0QSDotpCvDXL+uw7KEazn56adZNlSE8KtXafsTj7NUFY+nCPmaxs3V52N5bmGB5XQCPQjGWl4WdiYcCrGs2N1toLo65YoOMLPMvKalJYGzZ3U0NVmbalM6+9ZFRYvYuTNg8yxjMd5b0kiQ5O0JbGwQEFVdLe1+1UMPWTnpRpnZ782betYGC5D2EI/TBsmytGSvkhmWrlP84MgRAk+czjE1NQRbzc2xD66AQ4qGMjpK7p/q3b7yShx//MdxFBVxnZ06ZeA3v/HA601peZ465UFDg4WDB5lhKT/Svj6KLjjX05e+FEdpabqXpCp5FhREsX+/Fz4fy+DLy7wJat4Iwcxc03hvy8p4IE1PExxnWe58UmZlsBG+wSCDguVlavouL4tkAMwguKCAgKJ33zWwuKj4m+zrh0L0XT1wgFng/LyO9nYTk5N0OFlcJKr76lWixoNBaaOW3fR9VZD9xhseaJpEVRUwOMj3Yo+Yfc1AgGX4p55Kt0B77z09qd3MfaGiQiY5uqRa7dx5/w/GB6jUf6WRqSOZC9WpyO5EabLW39ur4ZVX4knNwhSiUjmvA+QLKfcMJwLxbvRK33yTfnFAqseTSHADUqr8mqYODsK5AYHt200IwY1raEhDRYWZ5dzgdi3KiSPf/XKiFw0DSSf21HD7nFy6jm7vWV4uk8IBDAK8Xi5QoveoijMzw7/zePgslFtHNEovv8pKKw0J2NCQwMAA1W3q6y1MTirrHg0eD9V6OjoSGBwkEGp+XrPLk7OzwPvvGygpkfB6LXg8BO5UVZl48skEvF6ZpkdbVSXx3nspFB/A9ygrYwmwudlMc3s4ccLMqUkphGUjJ6uqTAwNaUkCOV+7sUFQTmOjiZ4eHRsbRFfX1fG5c9On5qV6X4WGjsVgW2etrLCUWVKSuoaiIuDCBQ2PPGLCNElDIZ2J1QPTJBr3/fcN1NWZiMWo9rO8TGWkQ4foON/VRU5dbS2DAymZ7SlH+85OEydPcn6o9RgK8TW3bumIx2li3NurYWEh5VC/sMAMdH6e4gXBINK0b4F0/ePqatIqWlvjmJsj2AYAnnrKRF+fhuvXPdi+nYeJUlXyeBhEXLmiFG6YPS0tpVC2ah47fTLr6ojoXFujNdbCgkBRkUg6aNDwuL3dRDwOtLRYyR64wL59Ejdvapifl9i7N4VUHxoSOHXKi0cfNRGLCQwNEchkGAT4qUO3okIm2zMCp04Zaah4j4e6srW1sMUkVL9yfZ3rjO0RkYZ4Bbie19eppgSwRLy4yPWv5tb9Hg8yxvs8VGSzFSIvALz+ugfXr+tpLt9TUwTpKPSnU6HmoYf4P6cGZGZW6jQ/zq9Xmiq9quxJlTtaWsjHI4Rd4saNVFmrtha2x93qqkBVleVaEt6Kso7ztZnoxaNHE5id1bG6GkFZWdGWSs+bvedzzyWwfz9/dv26hulpbohCEGGnbKkaGiwkEqQozM5qNrp2dTWlnOLkn77xho6NDfa4AgH2eFZW2LP8y7/cgKYJfPghM2DLEqirY0YzPKzZPZ2VFYFQSMPBgyYOHpSuZepAQGaVuEdGqJ25vs6ej+KOhkLswZ065cHysrAzT5XRRSJhOxMPBNjLUqV6gIGE389Mb2qKEnuaJjA1JeyMUtfZt7x+XYffzwzKNJEmmjAxwYzm4EHLPsydYhVDQwSPAPysoSFhq/Ns2ybx9tseJBLc5A8etBAMChuw4kQ8Tk2xdxUIMIh76SW6WCws8Dmr9djXp9mi9N3dRtK9QWBxkX8vJVHcCljjNP5VAYaaW/399PtcWCAqV4gwbt8mj/HwYV5/ZSWBZdGoqtqw5xyPC5w/T/FyJf0Wj/PaJyaICu3q0u3strWVwdvwMA3NvV6qOjU0MPCREkl0t4THI5J0K2aPvOfkTXOOs3qhQHrhMPWFpeQaoMSbREkJkby9vSkxi8OHSYdytl6mp1lZisVY/gyFUlJxRUU8UJVzjK4DVVWW3SZQvW83k3ZV+n6QMf47GVv1YLx2LVWOBDghl5YE/vf/9qCiIrejBpDb3aC52UoeSO5/mxmB7t5t2tmTimYBCmPHYgJPPWUiFjOxe3fqcDt0yMT58+ypSannpHA4P4vSWUgics2cDhsAN4TuboIQ5uc9GBnREYmQUxYOe9LeI9/InVEykle6q1IyM6irI9+wqIiAobo6E3fu6LZv5e7d1MhUHnlqOB0OKD1noaxMoLnZSvoJ8v7Q6UNgbY19MU0jRaOnhxtLPM6Npbk5dc3OedPQwOvr7U25ouzdayKRYKan+HiRCAn9FRUS1dUWensF4nFmnoqX5hxer8QLLySwtCTwxhsGEgmJ0lILo6M6lpd11NaaWFsTuHSJAUZhoQVdBw4dsnDjBjeyffuYhVAkXeJnPzNQV8cDpLKSTg6WleIyPvSQlfTsJMduZUXDyAh7UC0tBL1sbAjs2WMiGJQ4ckRlXDKLFxiPk9MKCBgGnWIUIEvdO7UeS0qkrT9rmjIpWG0hEmHGcuGCnqxKEJyWyUV0jnhc4JFHTLsiMTioYdu2VOkTIEXn4YdNjIxoEIIHzdycSErUsXQ4NaWhosJCQQEDneVlgS99ycTMDOfI5cs6Bgd5aPn9LG2+9locP/yhJ+l9mbqm06d1xGLkCq6tsWen+Jler4SUqXmi6CQE87A9cvBgSt7RNKVtvzYywjbK8DB7vI89xmd644aO4WGCeEIh9jtra4HeXgaT7e0yCeoiuKagIN1txVld8vmQDOw3X9sf53hwMP4rjc2IvGpIxz/D4ZSPmt8vs2ydMsfdWEKp4TxMOzosbGwAb7xhIBwmSq6lxUq6CqSoHW7WPpEIlUSefTaRRMRlf576rPl5cjhZKgZisTimp2nh09VlpJWbf/IT+vJt25Y67Ht7Azh0iFG9lLRpKiyU9ntMTWlZVjbqwP3wQy15TwXa2kx8/vMJHDpkpZGOf/c7HaEQOZalpdxQQyE+q8ZGbg6BALl1pCukiMdq7Nxp2aXl/n4dUtIWSokIAE4TWAt9fQwGAPaffD7SNEZHydl76CFhl/Ru3iQt4NQpA3V13LTm5tj72r6d8+TiRR01NQl8+CGFGRYXgY4Oy974hoZ0hMMWxsY8+Pzned9XV7W0g0o9u5deiuP//B8PfvxjA5GIhrIyEuI3NphZr64S9v/88xQsiMc1SCnscrxpAvE4RawrK2m/NTkpcOGCjnBYoL7+/7H35dFxlve5z7fMPhqNRhrtlmTLsmXJNsY2xmYN4JDSELJdIL09p7dtKDlJbrhJQ9ok9HBLGg5Jk9s0KeH00JD0poSUQsitIRQSYrPb2HjBmyzL2ndpRhpp9uX73vvH873fjGyzJLEDJPM7hwNIo5lv3u/93t/2/J7HxA03FFBfL2xR3Lk5OoaZGQ0dHSQeb283cfAgx1gWFooBZWmgcOGFBh56yIGBAZZjibpUsLioWTy7JBugZBnZlQIBgnKkrNbMDGdnOejO+cCVKwtLSCDOFtSejRzD5ytgbg5nPPtuN0ksgkHuz2QSOHJEg9ttwukkUts0FYsfFdi0KY+2NoFTp1Q4HJw3jMdNdHez3/vjHzusXjXssrUsjfb1cU5y+XJgYIDl7nicvT5NY5tEAntSKVi8sKadMabTvOZkUsGyZUSv5vPA0aOaFVQQb/DccywB9/aq6O4mg011tWljDzo62P9VFAqoZ7MC0agGp5Pl7IMHNUxNmUue32uvLdhr+/OfLw2Wz6eVS6nn2WTK/3qUbqeXAaeneXDrOh8ckl4DK1catqyTHFIvBefE40Ui7rk5Dty6XCwVSUmo+XluXkmQLGfLSnkP+/o0S/+PvYJkUkEmo9qRoqQYO13aZ2yMpaOKCuCVV1jKPXaMw8Zr1xZRnbEY8NxzHPSvqqITGB/XsHy5gf37OQpQWm4+cYIDvnLEoq9PQzqdwvS0FzU1RYqobJbO8Ze/ZJmpt1fDz3+u4ac/1dHfr+LECQIaXnxRt5CGLCHt3EmR2RdeoLRTKET1DDL4C2tOrVgaJoimKJzq8wmbvLm0JO7zMaqfnma5jAw3DARcLoEDB1hu1HUCiOQ4CkmYyQnJ71Y6owfs2OGwqbcYfDhRW0tnMzvLftLKlSYuvpjOLhJRbNFc02Qa0d/PvbF8ubCcsYoLLyxgdjaBTCZwxixdPK7gZz9zIJ3md3c6eUhWVMhDlKMksrIwOsqeVCLB/dPTQ6fjcJDQPB7nmIfPJ/C+9xk27L+jg9+jVBRX03hIb9hg2oHBG5G6BwLkgI3H6bSjUdXqv/Nzx8c5m2eazJiSSY48JZO8XoeDfa/mZhOtrTzEZRujoaGUBpDVnNLnsK+P96WUHCOdXoSiBKAoypJnv6dHw9q1hjUWQ7RyICAsRhrO8y1fLqyKiII/+AMiNyMR8gk7nbCJ4/v6eD+lUPHevZJQw4HhYX5/l0uxdDhNTE6SwnD5ciJOa2qY5WUyvD9dXQwU2Jfl+SEJ1z0eIBpVMTFBVHo+z2eppoYjWSMjBAs1NNDJEmTG///Sl3K49lpm034/0NzM+9jVdSYaV4KgXnpJX1KWl/y1x48rSKd9v5bE3luxsmM8zyYd41uVdQqHhd3rGxxkKaG6WmDr1mK0f/qQ+pNP6nj8cSdGRxWr70OHUlMDe6DX7QYOH9bsbE8iFScnFfthLtVXlMwYMhqsqRFnKHeX9gyHhlRLF5DKA253kSg4naZDPXJEs0i7yTWpKOx3pFKwe5zr1y8lmD55kkPJUsetp0eD05nC2Jgfy5fzunUdNjeq1P8jYAi2okMyCRtVJ7X1ZElodhZYscK0FcIbG/lgzsywD7J6dfE7v1XdytIDOp+nA5UlqV/+0mErbZgmD8EPfSiP976XvKDBIPtgAwNE8W3aZGB6mqrtLhcPEqcTeO01HRUV7EetWkXkZHMzHSrRjOz/9verFjiDfTePhwfVyIhqoWT5+iuuGMU11wTP6P9SjUKBriu2k9Y09r/J3MT3W7aMYz/RqILpaQ2vvsp7LaWHgkHS8k1Pq2htNc9ApUajKjZvZtl/2zYDV11lYONGY8lI01shdT94UENXF8vcyST7bgD7q+3t3KvDw8yOs1ke+u3tnL2UJWgOm3MmdvVqztWWolEl8YDHU5zN3L2be394WLWJsRcXk+jo8OHyywv2WMzoKANJOdta/EwCjVas4Pzg1BRBPhddZCIYFNZ4EtmAVJX9fqlo4fFQcSUUYllbBp/hsEBjo2kFYMIiERDYutXAli3sQ4+PqzaX7YYNJNPXdVJWtrQwWGluFhafLu97PM5rjkT4t4ZBgJXkgGYvkcHvlVcaSCbZr5XjMJddZlhI7CLuoq+Pc6yGAXvGt6eH92fVqqX8tdlsFvX17l9LYu+tWLmU+lu0t6KE3dREtObBgxpyOQNOJ3XdZAmnp0dFOGzYGyGfZ4nQ6SRLyokThFY7nQQxtLSQdHpgQLWU11Xs3MnSX22tiVhMOYP3MJ2GHb1efrnAyZOqlZEor3vdEh3Jvp+wSqREAkYixVGLV1/lZ+dysMACjBpnZlhSOxunqCipL1dWktWkuppOu5S9h7NvZOeQ18DrYAYdi5ENpFDg4Z3Pk2SbcHVjiUJ4Z6eBlSsVXHddzi7FPvGEjtlZghBI5aVgdJTrd+yYE62tjNpNk+sxN6fg8svNJf2e/n4NhQIz0f372ZOdmwP27nXhhhsKaGkxsG+fBk3jOlVWmrZSe3u7YaP8AMAwmIVKhQhgaYlP0wjRb2oyoaosCcZiKtrbCxgZ4Xft7KSzfv55He3tDni9S3u8DQ0mdu4k4bNhSFYfjpFEIswgg0Ggt1fByIgDdXUET2zaVMDgoI5ksgggCQaBeLyoaBEICFtnTwJuolEF27cv5XYtRTM3NwtcdFEWk5PqGUjr0r0o95FpskdOMgsVk5McP9B19tMyGe6fri4eyNwPsPu1DQ28j6ejUeXoj7yP4+OK1TMkyCadJqipstKJ//bfZH+M8mm5HAPYw4fpIDUN9vV3dxsWQIZsT7ffTiinRLzKak5PjwqvV7HnMwEGr5EIy9cLCwwCGhrY406l+Dy0tPB+19YKu9Tt8RDIMzDAUZF0ms7whhuyS9oggI4f/1iHz8eAVgiWxuU+I8WhiUBAWJUgFbmcidpa9iEbG5ci8uPx4kxuNKrg8GGW4Pv6gESC1IdHj6qW0+Tco6py1MPhcLxhr/c3tbJjfAeadKCyB1UKUpiZUXHllcVNMDCgQlW5YaRW3/g4y4QeDxUwqqoEBgeZDXV0UD8vnSadVDBY7BUGAhSEBRQLecey2Omb+qGHHLaGpKz5X3ihgZ/+1IFkkpEkOUF5uPT16airK+Cmm/L4xS90uFymTdPFMgwBJ9u28fsWCrCh36XjFITBmzh82IFNm0xMTalIpwWEUNDSYiAa1aBpAum0ao2S0PGqKsdLpLP2eIB8nk4ln+fDXF0tsGmTgVOnWB7btKk4+nK2ERpd5/un05IkXMHjjzOT3L7dQDLJbNnjEfaYAkCnVSgIPPaYA8kkwQuBAHuYo6NkWwmFgOZmw+5hjo2xj+p2U2RZft78vIKpKaC+vrh3SvvWpWMcdXUmGhqAw4dJfNDSImwtu1SK4xkvvxzAiRNF8NaRIyr+6Z+cEEJY9H3cZ6GQaWeCGzfyIK+oIAoyl6MDWr3awMqVppVRsIwZj8MiLeD+6OoSeO45lps5zA88+KADL7yg4ZOfzFkgpdcLKF+fx7ahwcQPf+hEoUBtxHicJUe3m3JfEvXJcqSAYXAM59JLDYTDLOHJPv3+/ZoNkIpESN5/xRUF9PXpGB9nuyAYFJbkkrDZooj0NBEKFZ38M8/oGBhgj7S93cSJEyrGxig3NjtLwu61aw20t5twOEybSxTAEgEBAlg4DL97t2bPwPr9Jg4dYgChqrx+WT71+6lSsWmTsPfJwEBx9EgGAmvWFNDcLM4YIZM4hNZWjhuNjxPlGgwKhEICExPkN16xggxG6TTL7ZEImZE2bSpm9NKhjY4ye+7vlwLEDOaSSQWvvabaM8rRqILJSdictCMjgMfjRDSqWGNJrx+w/7r2riilzs/P484778Qdd9yBO++8Ez/4wQ/Q39+Piy66CB6Px35dLBbDbbfdhk9/+tP41re+hZ6eHlx++eVwu932a44dO4Y//dM/xec//3k88MADSCaTuPTSS60ewLm33wRWfLby6+lD6ozwYCEjGbWFwzykWluFrUje16eipsZEUxOjfurPkVKqupr1fTlQXaqveLqW4NlYS6RgrGHQGciB9ZoaPtyJBKH4sn82PV0kKPZ4SHN23XV5jI2dybrzoQ/lsW2bYa9BQ4PA2rVjaGiotPtQssR72WUF9PaS+QVg2S2b5b9NU0EoZCCZVK3xA2aWEmSUzbIHFIvx4Fmxgg75wAEN99/vxNGjGvx+OqVCgWXMmRnq2fl8wMSEYslpEeCxYgWd5+kD4SdPahgcJBhCyhRJ5xgIsHdJVDIBJiQJgC0ldeoUD0f2JSlJ5fOxzC35OFUVdu/L7+foQjrNa9u2zcSpUyrWrSNoStICrl9v4tVXga4uRuL9/ez3xOOKlaHyWiRnZjAo8IEPFBAK0WlWVnJvHDqkIZ9nJlRdzddrGrPBUIiHsq6zRDk0RMRqNMpynscj7KBtaIgSRHL86IkndDz1lIbnntOwa5eGQ4e4Dq+9ptm0crKP+MILDoRCJhYXFQwOaujt5b5SVWByUoOusyzLUQSuaySi2Iw2ktRbagWOjDDrlrJXL77IysHEBGxN1aEhlhADAZJKdHWRx/X55x04fNiFqSnej1BI2E46EGB5kn9Lp+LxMDtKJBiISJ7aVErBpZcayOUIgFm+XGBykgA7MvSwrAkwUCRTDytHiQRHaqanWbpub6fzOnxYtXUzVZUjE3NzdJalo0elo2YsvSsWJy1nbyMRVi0kZeHcHJ8t2dvVdVatThclnphQrHYObLapgQENNTXcy4uLwMICr4dk+nSaHo8CrzeLQMCBQEC8IYXmr2vvioxxcnISk5OTuOuuu9DZ2YmJiQncfvvt+PjHP46f/vSn9utuueUWjI2N4ZFHHoGiKLjtttvwiU98Ag8//DAAYHFxER/+8IdxySWXYOfOnejr68OnP/1peL1efOYzn1+67t0AACAASURBVHm7vt4b2unR8ulD6k6nsAmIdZ2Ne0kovGkTh5Ovv56w++FhwuY9nqJqfX29gdWrTRuJeP31OUxOqra0VSTCLJUPAVGWwSABCafL6GzfTtmg557TEQ5zrkqWLJuaDCwuAmvWkM6L83uwy3U7dlDpoa5OWOARlrQmJ1Vs2lRAU1PBRpb29vrQ3Q3cfPPZ0Lk53HefEyMjKkIhvncux6AgHAZOnhQ2XZWuCyxfztmy2VkFzz3nQFMT1S5GRxX85CcuXHxxwS4Z8vA2UVEhLA5NRuoAnQsJ2BUbMdnSQkkpnw922W/tWvKqOp102nQSsGa9+JrZWRXXXMODad8+DQsLMusFfD7TdiorVgAf+UgeiQQ5OC+4gP1Zt5tl9YEB9qnzeWZRXV2E0194YQFCLC0X9vSoGBhwYd8+lj8PHGA/uqaGTszhoKPXdWbny5YJrF7NMitLpHRunE1judTtJgCmutpEe3tR0WPLlgJ8PuBHP9LR10eHFg6bVkZMB1UoFKXO8nlh793RUTL6jI8zYPB6eYhL1LXDIVBRwf6laVJZYnHRwMgIZaIKBTomn4/rWSiwdbB8ObPgZ59lZtvRQb7UbJbrOTWlYXGRhA/svRWQTmtwuZh5Op0s5V96KRljxsY0jI6q8Pn4HOzZo1kgGZaaAT47Hg/72zfdVLAlmDSN6NNIhEFSIqHg0UfpIDUNdtA6O0tiCIB95GxWQWMjn7V4XEVlJf89Osq9eN11BtxugYMHdVx4YQG7djmRTnPWtr5eotkFYrGiXNehQ+RB7uoysWGDgfZ2gdde435QFMUipmDZFiAlo8PBuefKSo7YpNPFFpC0RILrv2VLAc88Q4Fkv59o79lZCb5SUFXFsRmJ1CZAyoSuc70bG4tjHufS3hUZYzgcxkc+8hF0dHSgqqoKLS0taGlpwTe/+U186lOfgsvlQm9vL7785S/jwQcfxNatW9HU1ITu7m7cdddd+OhHP4rq6mo8+OCDeOaZZ/D000+jsbERnZ2dMAwD999/Pz796U+fl6zxXA+inp5FknmeB7KmFQ/lq6820NZWVOqIRpeiKaNR9ulaW8USMutslsoHUtrKNGGDUoJB9vckdZx0SpL+7YorDLS1mThwQEMqVeT6bGgQyOWo1k31dWY4N99cgM8n8MILjjOEaIXgISiBBvE48MILJKJW1QWoasUZAsqSHu3qqwn9l9Bzqe6QSvEBXrWKYyltbQKXXMI5xP5+9mmXLTPQ0AA8+yyzgkiE4Aafj05Mlk7n5mDPaMkB5mhUtXhgYVNxLV/OwESSGvT0sA+1sKDa5NaSwL1QYL8pm+WB53AQMDQ9raCtjf2iyckiRVp1tQm3W7GZVpqbxRIwQ3U1bMX2rVuLlIHXXluwlV0o4SQJ05MYGfFiaIhOhSVsOs9wmCXZ6mqBa69lwEU2H4IjSEJRnAn0eLg/DIMHaF0d+1+rVxPc4fMxO8jlCI6prpaUcyzHdndzVIWlOWbB8/PsN1GGiz3GXI4Oeflyfvf9+zWEw8DPf65ZQ+YEW5EQwbCo4pjVr1xpIpUi8pLUbKrdP5MzflNTqgVogc3xOTenQNeZRaZSkkSAfbXaWpZTp6cVLCyoECKPbNaFhQWCvKam6Pi9Xt7zw4dZ7vb5CMRZu5alyuFhDa2tVLIfHlatLFzF8eMEZS0u8tkyTWZvhQLXLx4nSfrKlQY8HpYgV60y8dGPGvB6WXE4dYolTEm1VlMj7KpCLqfY4sVHjmjW/C4/e2KCKN6hIVZ/NE1BS4uJ7m4TjY18b5+PYxleL4FW1JktiiKPjBQzfI9H2L1dXSciP5Hg95HIZyLj+fuaGlg9fAWqWkBnJ+n4zsd847siYzybxeNxuFwueK1J+L1798Lv9+Piiy+2X7N161b4fD688sor6OjowN69e7Ft27Yl5ddrrrkGd999N4aHh9HW1vbb/hoA8KYzdqfb2bLI++93IBJRsHIliYdLZ+8AzndNTTnQ2clM89FHWf9XVYF9+0g2LOv1pfNYK1eKJaAUyeN56aXF6yrtazU1CdxwQ8Hu0/znf+qYnaUz8HqZmbz8MqP+0kxRRsqyj7lrlwOtrQaamogs/OEPnVizhvRmx475oGk6nE6BRx5xwO/HktnHgwd1XHddHv/jfxQfmP/7fx3I54n2CwQIksnlFDz7rIYLLuDAeWMj+Tr37+chWFPDcqZpwqbASiYVpFI8XJcvZylMCJaLRkZUhMOm9XNgcFDHRRdll9y7cFhg5UrANJl99PfzwHE6md0ahoKGBgoQp1IENySTVGBXFIJDolHVUg5REAyamJujQgKwtKRUXS2wbZuBgweXAlUAvhfHBphpsCetWaTYHM9xuxkIyIx2bIwjGidPUrZM8qWeOMEgzeUit2U6LayqAv/u9tu5Bv/n/zgRj6uYnWXZ0OsVCIXo8NJpYTO9KIqwxxIqK4mkbWoSVlbOEuLYGLlodZ0l08cec6CmhqoWsrQXDNJZHTpEpZZslt81FGJGOz5O5K4kj/f7haXRyJGNaNS0M1bDoLMzDGYupkk0aWUlUFNjWNy6sGcPJyY4BxuLOTAzI8vEAvk8QXHz8/yMYNDEihWmPQcYDJLkg+0RkjLI/TczQ9CTpikWKb+wy45ECgsAKgIBA4uLrJJUVwOXXGLitdfYxwsGeR0HD+rI5UjO7XSy/MngWlganVxvWZ5NpYBIhJR5NTVEcDc0LD1rrriigIEBZYkosmR8SqeBn/zEiRUrTKxZY6CuTuDVVzVEowyoWIJne4DZPoNkapIqNjHDsmUCgYCJ7u44br01cN6G/t+VjjEWi+Huu+/Gn/zJn0DX+RVmZmZQXV29JOtTFAU1NTWYmZmxX9PY2LjkvcLhsP27c+kYpbM7dqwG3d36G7LOUE0d2LOHh73DwQj83ntd+J//M4v6enEGG0zpe5WqsldU0PEtLvKgrq0V+MEPHNbfFewyKaVqqKGWTpMOSqpSnM7So+s8wHt7FVx7LYe4T2ctKS1nSNAQwBJtLscoD2DJyecjEODee10A2B9UVUapgIn5eQWZDJlGqqs5kyjZ+EMhro+ck3rqKR0f+lD+jMb+6Ug104SNEI1GFRw6VAQskXZKhWmSGSQUIk1XKgULGSosRhxYow0C69cbuOqqAo4epcbgyZMqQiEDoRDXpKKCZc8HH3RgcrLITyoDlLo6E8eOaRgbYyl1+XJmZQReMIK//nqWj//+712W2gDnwk6cIIuJHK4GFHR2GpiaKiKMpblcAtdcYywBUzzxBOnZHA7Vek/TYmnxYNs2gUyGSGJdB9raDKRSKsbGGCxs316wS++KwgOwtdVEJAJLGd60nJ2CwUGSANx/vwOxGJ1tRQV5MPfv1xAI8GAGWJ7UdcUSTCZ1WFeXYWdfko5Q9sVVVbYNFGsUhWAsIYpBDQkIYM1FqvD7OXbQ3MxMr7WV6Mv/+i+WbFWVGZ2i0HnIrDGVIhmAy8W9sLjILMvvJzepaXIOk5qEJkZHyYsqCfZ1nQGV08lARdfZa1671kBTE6yxHFh0hAwYdF2gt5ffJ5vlM8rheGbycg4zECDmoKuLYyiKUkAwyOx0ZkbBtm1kdJqfZ1k2k1Hw8suqlW0KDA7qqK/nddfWcu0nJzkP29paDHAyGT6/8TiH92MxIr/7+1U4HIZNCAGwpN3fz1I62Ye4t7NZ05rJ5tmSzwODgxRAYEmbFYG2NvaWOcOrYtkyVq7icd7b668vYPPmKJqaKt76gfwr2tvqGL/61a/im9/85hu+5vHHH8fll19u/38ymcQf/dEfoaGhAV/5yleWvPZspVAhxBnO8vTfv97fSuvr63vDazzdpqcdeP75IHy+AjRNxRNPxPHDH+rYvDmOSy5ZRF1d0Yns2hVEOq3glVeqoKoqXC422KNRgcrKPO67D2hvz8LnK8DrpfTL4cM6rriCHGTHj/swP6+jqqqA2tosZmZcGBpis3xuzgGnM3vG3/l8PmzcqGNw0IPJSZJVLyyo2LNHxS23TGJiwodTpxQUCiqOHfPB7TZQV8eoLZXKoqsrYX+O/Nwf/ciLEye84CGdxKpVKczMuBCPh5DL8W/ZIwByOfI+1tRkMD/vxNRUDm63ibo6DaOjDkQiOqqrC6ioSODgQTfcbgM+nwv9/S4sLBTQ3q5gfn4OmYyKQsGNnp4UHI6kvaamCQwNObB6dcT+WT4fxIEDDkSjDvT2elAo5JDNKggEDKTTGTidOk6ccCMcziGVykFRdMzOurB8eQaaZsDjcaKiwkRTUxatrVl0dSURCOTR3u7A+HgQ6bQH1dUF5HIK5uYcmJri+iWTKtLpNJ56ykQwWMDEhAvRaAGxmIZwuID6ehV1dTnLKSSRzRYs8Enx+q+6yoP/9//CiESAUKiAcNiNQkGBYeQxO2silVLR0+PA4qKOQ4fyqKzMW7OaAsFgHtdfP4e+vjympx04ftyHnTuDyGRUBIMFaBqHwdlDdGFsLIvGxjwKBRdMExgcdMIwTPh8Bi65ZAG1tUlMTXFNHQ4V2azABz4Qw5Yt3POGARw44LHGWVxoaMhgzx4Hqqqo6BKNAlVVBQhBAeFUSsfFF8fh8Zg4dYp7qKUljZUreT9ffTWAWMyBwUHutZkZomRdLmZKkQjLwAsL7J+53SZiMfZldR0Ih7NwOByWmG8eoVAGmQxHdAYHBS66aAbJZAOiUSdmZlx2SbK2NgdNU+H3F9DYmIVpuiwybGDFiiw6O1MYGXFjctKJjRvjABxoaOCzNjNTh2zWgViMGZOu56xrFGhomEIgYGBoqArt7fNQVaC5WcOLL/Ke5PMCW7Ysoq/Ph4kJp5XlAvG4A4WCAiEMqKpARYWCbFZDKpWFphVQKGTh9WoIBAwIodhl45ERDb29HsTjGtxugbk5HcmkBpfLRENDHh4PpeZmZwUuvXQBnZ0pDA8HoapOzMwI6DrHRXI5N1wugY6ORXR2JhGLaRgZcaOnx4mGhhhqa7P42c9cmJ/n2TM46EcspsPhEKisLKCvz2uDnZ56KoVkUkM8rlqi4hmYZgUqKgTC4TyamrI4dMgPpxMIBhV0dMQhhAKfz4Dfb+D975/+tc7lUuvo6HjD37+tjvGTn/wkbrrppjd8TXNzs/3fiUQCN954IwDg4YcfXoI2ra2tRSQSWeIIhRCIRqN2VlhbW2tnj9IiER4+8jVnszdbxNOtt1dHeztLITt3JlBfH0BVFTA358OJE/VYvrxYF3/xRQI+nn7aaXMcCsH6eihk4oUXVMtpCFRXM+JdXAT6+8PI55k5NDUxS4lEVLz//XzvJ57Q7XKmNM5n1cDhIGx7xQqqLkglctMUiMV8aGkhNdfAAHtrjIwJ5uEBWoNbb2UGMj5OOqrBQdVWvhgcrISiUGW+sVHD6Cgb+Rymhj1H6PX6ABDl6XabCIc5guDzAc3NJtJpH+rrhU00PDGho7LSgdnZAqqrfUgkWGrr6fGhujqEFStYRpSUab29YTtTW1xkBh4MmvD5GInPzRHhl0774HAwyxDCjZ4eAgE4suLCwADHQK66qoDt293WvQufdq9ZpqupIapRMt6EQoDT6cbBg6rF+0hkrNdLYMHy5QIOh8dCdFaitpZZdV2dQG9v2OaUbWvjvggG+U99vUBFhQvPP88DxudjBhWNupFKEdARDLJP2d9fjb17qdfo8bCEzJ4gwUfMkoBwOIl02g8hWGoeG9NsFpJYTEM+X41UKoj5eYpDBwLMlrzeWvT3U3j2+HEOcFdUCKxfb6Kuzol9+zQ4HCQ0GB+nfBCrASb+1//KYXIyjNlZsjOtXq1i9WoNquq3VCI01NaSezUcJiWd280eXSBAdQgeAzqamw3EYiS/TqVY6Rgbc8I0+dqmJh2ZjNfmG21pMXDZZV4kEhq+9jUXsln2ayl67URrq4FQyAmXy4tt2zi2E4ko0DQvkskg1qwx8d73CgCeJc9aXZ3DknNTEIkUYBguqxIETE01YWgIlmqFH93dJrq6BCYmWC1yuwW2bHFi1SoFBw+y/2uaii0tlc+TAVyOx0SjXlRWGtA0Dz7wgSJl3fCwgp4eOsO1aykdF4vx2eMMogpNo/4igUgKli0Loq4ugHzeAdNklllX57ZQ8MzaL7ooiOrqSjQ2krnG5wMaGlz2eIwcs6qrI56gpoYZsdfLoMIwFBw75kJtrbCeN8Dv92DrVoGxMRX5vAujo35kMpz1rKkxAVThPe8x7EpKR0cAfX19v/K5/KvY2wq+8Xq9qK6ufsN/HBZRZzwex4033gghBB555BFbBUCa2+3Gfffdh6uvvtp2pnv37sX3v/993HXXXaiursbMzAwefPBBfOpTn7JLsA899BD6+vpwxx13nDPwjWT4JyN/CqEQI+j+fs3u65yuaDE2xt6EHHoXgpD3bJZMNRKUEgySBm33bmrPvZ5ax+kaeEARJCNntaSu3ews+yGNjSyPPv20A62tJqanedhLxW46ZUroHD+u4amnNDzxBHkpAwG+n9NZBKlQVNTAwADBOAsLLFctLKhLWDi8XpZrenrIvXj11SZmZ1UcO6YiHJZ0Zgpqagzkciw3+f06RkfZrJcyNkNDLBObpoKLLmL55dgxHUII7Nih2xRgiQTLPMuXm/B6CXiR/S7OvlHtXFUVa9xC4P3vL8DtFrby/K5dHB94/nkdQigIhbiOAOdDCwWCYFpa2BOkUgPQ3U1AB78z1+PkSZaKFxdVG/XY08OB/Nde022NvdWrTXg8Ct7zHqqM9PaqVlmR91fXhRXI0DF2drIMHY+z7DYwQMBPQwNHZsgdqqChgT0jlyuDVasc8Hq5/rW1AhdeSLo7Dt+rGBvTEAwKS/eR/cZf/MKB3btJSmAYikUKrWDZMu6vZJJjGVKpY+NGAp44f8Zy+WWXGdi82bRm71giffppDXNzRDl2dpr48IeZLbndsHus8Tg/jywrchidbEv19RxdWFhQLCFlZiiyFTAyQmaeU6dUrFljWn0trkk4zDJrdzfXZnRUxYEDugU6o+Pt79dwzTUFG0zS08PxDiE4NuL1KmhtXcTCghu6TmTw9DTv2datBiIR1ZpbZa9aKo6k07xXCwsskf/Jn+Tx2mvswfl8HCOan6dI8wUXFLB9u2Ez80g2IDlmBXA+NhhkOyOd5tiNw8FnqrmZ7DeZDJ/L117j+BeZhkzrWeUoh2nyXspZzUJBRTBo4h/+wWWVwmEFnCrq6zkWRIkxngmxGEF1msaSaCzG84yjGuyhDg9T1FgyZ9XWsrLA+WjYDEdloWLQKX7kIx/B4uIivv/971vExUkkk0k4nU5omoaamhq8+uqrePTRR7F+/XqMj4/jc5/7HDZu3IhPfOITAID29nb84Ac/wJEjR9DR0YHdu3fjzjvvxGc/+9kloJ3f1KSzY6aRgmF40dOzlJ8ykVCtoWtyqPp87LFls3SKqRQdZFdXAQ6HUiLPw0hdSvSczfFdeKG5REKquI7MNi680LB5W/v6yJEIkCFkbIw9AYcDVpbK/kChwAb6jh0OTE5yPmxigmgzEhcTbedywebJ9PsFrrjCsGD6fH0mw4csEOBh7XYzw+/tVREICFx1FQ+kmhoShk9Ps8fQ3W2ipYUzUx5PAnNzzDb9fsLTidLlQ7x9O/kfXS5gcBB46CEnBgc1eL08PFly42Fx9CgHuCcn+fDV1vL6YjE+oBKpunatsGc4JyeZcY2MkGVofp6BQGenaQnQEmyxbh2/59gYHZKuA7W17KtEIuzzcjibIxipFKPrsTGJcFXQ2EhQyvw8KcjSaY5SNDWxBxWJqBZXqWnxjxZ5Y9NpxQa0DA2ptmyVaXLkYnaWhxHHBgSczgxuuYXgL8MgxN/t5p6LRpmB5PP8uWkyM5ZD6hSnVuyDUPKR1tZSl29khKjfyko6jcOHdbsyEgySZk8+D08+qeOZZxwYGlIt0gAGMrW1BJ68/LKOZcsMaBoZaWIxFV1d1HGMx2GhiPnadFqxSR+kWHRFBYOVhgaOUkQiCtJp1UIvM6hoaWGPkZkpg46GBt6nsTEV9fUcvaF2I5/bqiq+1+AgHWShIJBICFRW8vlWVTqztjbTnnc9cULFnj2ahbbl55w8qVnAE6KRk0kiyBMJypBlsyRn2LCBo0bd3cW+vcPBZ9ww+O+xMRX79jG5oOJLcX9UVMAGIkklETkDPTOjYf16E+vWmXZGuWmTgVyO5OHHjmlQVRM7djgxMaFa54xiP/dOJ9Hzg4PMeCnkzP9PJBR7VMvtZm91YoIIb9Lb8flcs4bI2sVFBSMjBAhKesV8Plp2jHv37sU3v/lNRCIR3H///bj33nvtf6644gq0trYCAN773vfi8OHDuPvuu/Ef//EfuPTSS/Gd73zHLrm63W5cffXVeOyxx/C1r30NL7zwAm699Vb85V/+5Tkd1ZCE4YuLQCKRwdSUF/k84eGl+n6SG7KuzrQgyMLm8hQCeN/7DHR2Cou+i9H39DSzk6YmUjr19Wl2pCqVusfGmDkdPapZGmpLOSWbmoQ98iE1AZuaOArwyisaUike+ooCi4ycTuzgQcoPSfi7hMKnUsxohCAsP51m6bOzs6i7ODFBWaGRERWaxgMb4CC31G1bt45oumCQDrmpidnTxRdTRun4cdUGKCQSTtTX0yHU1xd5OJctI+Q9GlWwZ4+Op55yYH6e0PLpaTqkUIilwmSSWX04LCzKO2aTtbWwpIfIP5rJMIOUyvMnTzID9nphUaNx9MHpBC64wLAItnng6zoPy0hEsdTReXjPzalWBs0ylarKWUhhCRrDGqmBJdhLkobly7mHWltNi0uTagcScCX3T3Mz+2hytGZ6mv/tcvF+UbGCAYGiMHubnxcYHHSjspLB2/AwRwP6+/m3+TzL2vPzPNh6e5nlclyB2Zbfz4PW4+G6cWaPgBYhFLjdCk6d4sB9QwMs1C/bBqkUs9nHH3cgm2X2TlIBOsVsltlKMMiRGl3niIMMrkyTwJxcTs5CwsqmmeF4vQR5jY0xQ2xt5fdPJOjc5+eBtWuLwc3wMNVtyBFKekWvl2CYSIRl12PHVKxbx8AskZBoWTqGNWtMjI4CGzeygtHfTzKD0VEVp05plu5jccC+UABefVWHqgp7Hzc2EuFMlipmu6OjDIjm5hQ0NXHf9vXRQU9NKdi7lzy1L76oIZEg8jyRYJDp8xEglc+zGpVKMSBbtozoVE1j1crhYGUjk1ExMwNcfXUBus5MlGhg4PBhfudAADZRhdxLo6MMaNetM+HxEG0tEd6cV+RYxuIiyQfIV0uCjbo6fk8ZSAwNqfZIT2+vip//nO2UtjbveSEQB94lqNTLL78csdMF785iVVVVuP/++9/wNd3d3fiv//qvc3VpZzXJ7/jMMzp+9jMnMhk6CVli7Ooyz9DUa2oq4Prri+9R2iPctMlAfz8P9poaE9ddl7dRncEguQpfe03F5KSOtjYDV1xhWGU3gX37NBw4wMOxVCOxdORjbIwMGqQPEzbv4Zo15GwcHGR5UteZBe3dq9slnXye0jWAwNgY0N5OJ7pihblEHkaWby+4wEQkUlQ5kMoTU1OcT5NSTdXVFD+94goiaZ96ij2YVatM5POmDX+XD4akeXO5ihyQk5NyLo66iJqmoFAgmXlVlYCiUKevrk5YES2RqH19JDSgWK5i9y6l8vziItdPDndzaFzg4EEVPh97i+PjKiYngcVFDfE4S3eLi5xVZM+GQRADDAGnk4hNjweYmuLBw4yXBxh1LgmplwebYbDHpussB09Nsc/MPqSwg5ZLLzURjxsWKxDfb98+DePjzFZdLqJ9k0kT4+NSkUKx0Ihc18lJjhZ0dZlQFAUHDlBLUDrCIs+ugmyWTkxVeZCFQsIaLSFgxu8XCIdh8eUy0xke1lFTI7Bzp2YRT5D8WvY/5+cZ+GUyCq68srBErm12lv24YFBgZMSBQIBkAZOTDELc7uJ6BgJ8/aFDGvr7hUUSbtjkAv39HMqfmtKwalUBExOsEExNqRgdNe3epqqyv37oEBGolZWk2pufBwyDzjGfVxAK5TE/78DiooJoFJaYNB3Bnj2UaVIUBW43lWncbga2wSBHFebnTTz7rI7hYXKiTk0xu/R6GYA+9hjVVTIZ3q/paR26btqqGtEobE5kv19g+XJ+1wMHWC6mYgnL0mTLYYWhqor7sbnZRE8P502fe45zxFVV7GEuLrLcnk4LZLOqhR9QbAaj4WEV4+MsIVdVMRhNpVTMzLBn7/EwOzUM1RpD4bN0+DDHgeTMtWlyPz/3HNe6sdFEPu97Qwm+39TeFRnju9ECAVhSNSNIpUJYWGDG0N3NzVgqlXM2K5Wpqqzkw0imCbJivPKKhvp6HkDHjrHR7XQWI7OpKbL8S8HT973PWNJzO3CAYwZ8QHVbompsTLEYWDgQHg7DLj/xWlnWUFU+5LkcszaPhwCXpiYTmzeb+OAHlyqHyO/j8QgcOKAjn6cWm6IQ2OF2MzJMpSjaSuo1qtc/95yO2loTK1eyDHf0qIJMRsfUFKNVrxeYm+P1LFvGDFtmQwRwMKOiPh/n13I5jj/U1vJzSWtGhpXRURWdnTyoRkc5pFxfX+wDZTLFknEuBysKZ+nt2msLWL6cB8yRI7ql6cjvf+oUMy8qUijWNcFSNlBtkFE+D3tonCAlYYFOSDMWiwm89JKOeFy1nCVV3AsFlsZ0nT0cXefB1tDAoODgQdLRqSpHDdgPY+/G7weSyTzSaScGB1WEQiR/GBykbqZUco/F2COTg/+FguyLM1OLxVgNCASANWsMVFTwYKMOIku38bhiqTEoNkMNB8i5Z4NB9jRHRznPNzOjWmoU7HlKpQmAzuz4cWZdVItnlSESIZqTDEe8XwsLRQq+ZFKx+75zcwwEfT6WlF95RUcgIOxMknJLnHGVhaVoVMHwMOc+FYXrd/SohupqrgWhEQqczhSOHfNZZ4Kwy6GSjN0wCKAri3BOIwAAIABJREFUFBiwsN9HxxSPK3jySYcdqCaTvF8EwxGo5nIJqzTJ99I0ZnqS9lGOdcj5RTlis2IFWWxCIQ7mSxahSIRrwx6fsCofBk6c4GukKo4UFFAU7gkpgpxK0bGuXcusXtLWkWCE+0C2agBYDEksdZ88SYabQoHfTRJgSGYv0+Tzns+zOtLVxfPiXNPBAWXHeN4tm53F1q1VSCQIuBgdVe1m/WWXFdDYePZo53SGG8MgOKe6mhEbh4BZlmxqMq35Q5Y3k0nFItfmIXDqFImGczlG8y+9tFT26MgRzVY6kHJNpsmNuWIFyyHRKHlBCciBBQjhg9HRwQz4Ix8p4K//OodwmHOXL72k4fBhDa+9ptkakw6HZLYheMPjgRUo0OFUVgobvfYHf5C35i4VhMN8OCgXlIVpOm2ygZER6kvW1BhwuVgelTOK+Tygqqo1ryiBGIxAs1mWVuvrCbCIxZjlkYlE2KXBXI4H+saNpjXzyEORtFYErwwNqVixwsC6dTwopUONx8m209TEnzmdwLJlPIRjMcU6rBX7tZrGshYDjaKTyed5GAaDLHkzKOFBL4RqM5h0dgq7hKsoLM/NzlKPUhIoBAL8HF3nWrO3pWBiQkBVdaTTjNiZqdHpJJNyX8EuOUtmo5oaYVOiORzsfy1bRsHi3l72nVpb6Yw58sMDVB7wExPc28uXm/b1JhJSNFmxSL8VvOc9BhSFDs7t5vq8/DKDu5UrCYxKp+noYzGuD6XN+L11XWB+nm0JOXohAUNyrUMhguMotk2kNvloYQFqYLEPcXies3iapf9IpLPTSdkkOrMC3G6S6wcCiiWKXOSfVRQyQxkGbH5hw2DAMDVFhhuOaShW31KyKrE07XQqdmlbcv8CRWyBx8Prqqhgf/uiiwrQdZa0jx3TkM2ySpFMkkhc07j+1NRkP1TTFOze7YCm8ZqzWcV2vtGogkCAZ8nCAtdzxQoDAEvwUssxl+O9bGgQmJkhWC+XY7tAklmQO7kotqyqkrgA9j3LZhWLFs6A38/K1YUXlh3ju87m5ubQ1haCEBTRjcdZVmxrozr56ZRmpWTIpUP9kopMolBjMUaYw8MqmpuFFcXxwQyFyNSRSLAP4XDwUPJ6gUOHOGe0caNpo1ljMVgzVPJ9+aCpqkB1NYEbmQwwMaEhleKDurjILGLFCgJlmpsFPvjBAvr6VNx3n8smETh6lGz/sldw/LiG6mo29Fta2FuVMkQSXXjjjQVUVgo8+6yOJ5+kMG8sploIRAHTzGFhgTNnMuBoaxMWKbZU1uBBe/KkjkSCr4tGFQvGz5KVy0UnQyScYgEPFHR0sH/X3CzQ0iJHUHhfSDAto332wLxessAEgwTlDA4SZh+JMMptbxdWhsBIO5OB3V8htykgBYmphMH3n5sjwri+ngGMEIpN61ZVJewDU1WFhWhWbEafsTFeZ2MjnZaikLWms5O922CQpVyJ6uUguQGAQs8yW5bk9FIwW9fpHGXgQBoygpQ8HoGPf1zOcqoW6bxq9Qp5ONbXEzktMy0e9orNuEPnwlK2YSgWvRyd6NGjJL4+fJiHLjM0E5dcQvHdSIRlYJYaGURQcUZYVImUXquthZ2l5fN0kOxBUkMzm6VTjURUe6/Ikp4Q8n7x92TC4QGfSnE9ly1j8FooAFNTAtXVuk2a0dbG+8N+q7CG9vl+kjSBDpx7hG0A2D1XeR9SKcUCRsEOUITgHpPVHJktOxwMCuvrTVx6KTO7Eyc41iKzMBmQTEwUBaUrKoDdu3WLrpBn0Pg417C+nr3YZJKjNFIpR+IkBgZYzZDlfOrCFlVtpqcVqw0jLM5aBiZce16PJA6XTj+VgsXFC9TUpDE56cKGDeaSFtG5sndFj/F3wSYnVVxyScGWsxkYUDE9reD4cScaGgRaWpbKOslIUcocDQ2puO66AjiSwQewp4dKEg0NdDyVlWS8yOV4WEsy3poaWFIwAhMTBKIAxeuYm1Owb5+OYJBsKrOzPOwKBYFjx8iDmMsBErXm8wkEg0SxVlYKe1zg4YcdeP55jjcsWyZw9CgPC4dD4KmnHAgGqW13/LiKWIxis01NlJ+RaNtcjpnk4cMahobYn6JahIZ8XqClBRgfd8LtZn8ynSZABCD/amUlwRgzMyr27NHR0VHA4cO6zbfo8Qhr2FqxA4NkEpAADZeLsH6fj2TKHg8DA1Vl+VIykdTVcdQjl2MW0t1tQFFUvPYaWUxk2TEQYH8ukWAG7nAUIe/y891uznR5vQxM+voI1AqH84hENIsyTEE+z34Pe0+K5aDlgaNAVXlASGkl8qYKmx6tr4/gpfl5HqLSYjEe/H4/EAhwH05NqRaVl2L1gWCjTV0u+Xmwg6h0WsX69QWcPMnxB6pHkF0llVIwOAgUChre8x4DK1dKRKxqAzYkElbypcbjqrWPeU/icd4zXefrkknTyrgJOJmf5+sZEPK65ucVW2asogIWyT4ZVTgXSa1L04TV62NgKQQwMcFZQcn6ks0CW7fm0dPjQCYDu5wus3bOGbLysLDA+0x2HtMCFqmW9qlifRYdgtPJtVZV9iQ1TcDtVu11i8WK41js/xWdxeIi/9vnExbhA+9n1mIh9Hhk1aTY4x0aUvDSSwxYKyqEXZaenaXDk2MSsjqgKOQwbW83LO5TkvLLQCufNy2AFxmuxsYYmMhAMJHgfLDHwzWLRovBdXe3iWSSrRnDKDp16dAVpYhuLt2vTie/j9ShPB9WzhjPs83NzSGTqcFDDzkwPk6F795e1XqQgCNHCHQg6EXDwICGgwdJ1NvUxKitUOAskpTu2b9fs9TneZAOD5Pu6sorDas8odgK5Koqyzt0GpK3sKHBxPHjbOR7PMDAAKNYj4d9KjkaIJv2tbWSBgtYv960gCOyNMJslmKtmsV3KamueH2JBB8k2edRVUaA8Tij4uZmE/X1whpI1ywKK37myAjh8IkELISpifp6BaOjmg0UITUW7Lm3qio64eZmUpA5HDws02nZ6xA2XZcQfHiXLTMtrkqyqUQi7G3JEvX8PEtXshy4YgUzyHyeUff+/YSiq6piZ2PpNJGSmQzLVQ6HaZeXNI0PPWf/FKv3SHo4vx8W/RhLfUKw/8P+HODzKZaIMywHU3QGMzMsVcvvJXtChw9zZEZ+ZjRK0MP69XS88ThQU8OM6dgxlt9lSUtmJckk7ExZ9ku9XthqD3IIfm5OtcuuyaRiA4+mp5nNyvlYkjSw7Dgzo9oHMkVwCVpaWGB2IbMsOYZRKMBy3Io90xiPK9a8Kp8bSTqu67CCNKJ1ZQlbvk46eelU5CyxJKVg6VmxhapNU7EIyYV1HxWbNo1ZNLOraFTB2JjDClToKOS4VijE/rPXKyzdQjpav9+0Cemls2NgWnQSstfJHhwzNnKNFn9PMAuvu6urgOlpBb/8pQOLi3xGCgX2WWdmFNsJOZ2w2xDJpGIhxlkiNU2W1jMZWBUjgbo6Bh2y0iQzXKeT10DwFu+PJBfI52EFAYq9p2WWKIMwzqkWHaL83j4fv1cuJ/DRj/L9zkcptZwxnmebnnbgxAkHcjkeTmNjLK9t3GggkeBQbTZLUdiNGzlXNTCgWg14lgMrKtgjeeUVDUNDPHjJkUmR2vFx9lf6+oh2NE1ucrebka8UFHU4gDVrCpiZUfHCCxra24UFMedBE4tRTb6+nhvf4RBWr4O/7+42MTYG7NnjwPw8P2PPHg0OB3DxxaZdJo5GFRw5QmCCptF5+P1kPYlE+AApCmxHnM8D8/OU8PH7TfuQ2LKlgKkpkiG73YollcQRBV1X7L6k5D8NhSiTNTGh2dRehsGSoMfDBy0eZy/E5ZLE2cXMp6dHs7IRHlA8MImyW1hQrLlQgZoaHnBTU4pVJjYtrToJkIDdx5yZUeygoKqKOniZDA9DRWF2k0rBospjFnjqFCy6Ow2VlQKTk7KcpNgKCJkM4HaTKAHg5wkhhXKFHRT19WmIRDhbS8UNYe/FYJCH+vw8e2ZjYyQNkE5HOsRUCrYIsQRNyEOUByyh9fk8LCo6ZjosiUpBYP4zO6tY85QsKVJSS7XmDmGP+mQyvJeGwXIES3FFrUEewIrtTNxuZtEA7PIo2wFSl7NY9pXXEo8XS6N0BPx/WYos/ZlsW9BBKfZ7S5SvaTLDdLsVxOPC7hcD/LypKQ2jo8IuGdbUmJiYUOygTgpKSy1Nj4dZk2Hw+5QaHTLs53x+vvhz9ouLIC6J9Dx5kqTh2SwdVm2tsMv68u9kKZbjQbC1QN1uniHRKCwZLn5eJKJidhYWmlWxxZ9l1infWz6jCwuKzYDD8SRYvMjF72aaZ37X0mwxl2MpXNeJpi5FJ59LU2Kx2PnLR8uG+++fhWk24oUXNAwOanb2pqqwIdHRqGIRFnNjMStiiU0O2rOswQxycpLNeY+HvURAWGz7fM9QqIhmzGbZd5JlmkKhOGhL4VgF4bCBo0d1O1pmb4iZZF1d8QCT82KkH6NTzecVG9YtyQnkDGRTEwkD5uZggRf40DqdfChkNuJ08kHPZtljJIKQ19LUJGz9OZmVCJGH3+9APl9EhiYS/AzOgAqLW1EAEHaWBEgNvuL3lDNYMrvgoLqwQB90Cool4ZRI8KDw+/m58Tg/W2rkEYYPy/HBUs/g4V1Tw+xgbg72ML+8JhkZy7WQxgOpmK1pGqw+FP8/k4ENzvH72eelWgZsRXeZ5UhwA/cV15elY/4/o/AcslmnvQ9ktlJqkt3ENGEBW4q/kyAJ6VBlBiNBFDLz4ogLPzeVKpYxZf8sEODPNI1lXnlAe738W0krKO+hLDPKrOlsJgfoFYXfgQP0MqAoOlGZBQPFLE06Svn9peOVDkruw8rKIuG3XB+vtwAhdGQyxZ8ZBmzgm/yn1AFI5yT39ZuZ1PQsvQcOB9c3kSj26qSzU1XuYYB72DD4egarRbS5DI6kGJFc3yKyuhh8yqDI7YYF+iuW32WpOp3mPeTMK1/zevvsbPdP3iuHA/D5CujoUHD33dmzKhD9plYupZ5ne+aZHMbGApicVC1lAZYNFhdJWdXWJnDokGaJunLzsiTBbCUYFFbGpmL9egPt7aYNfFlcpFOan1csoWLYzCdSB1EOjGcysCjYGOnJxn8oRPVxRnKKpY9XLMNIXUFGkxKJykiX8G/YUbtUqReCWevsLD+nspLXmM/zIZPZh9zskuKL4Bth04rJUpnsFQEEbVBDj01/TeOaSudB7lXqxBUKFKeV6FR5OMqHmdlOaebD14XDzM6kTpzs6wDFTIR9rOL1cuax6Ei9XljlQ/mdmR0Xy4LFHoo0eWgCvB55mMgDXV6HLCdJIIIEt0g6O6nGPj2t2ixEUvdRHkilh7H8Tm63gVxOs38mP7c0YpdOTDoTh6PovOXPZZlPfjdZFZDXL7MIXef1l5Y9ZaAjQSPy9XTcxUNXUYqfX3qd0kGczeRgujyoWR4tvpd0jsDS/wbOdC6S5kzeL2Z4wi51SweVyaj2vpHrKPdgKciEMlPFtWNl4K2cMMUASz5Ppc6/9DuXrn+pM5KyVTIwk/tD/r90fPIeyzWTfyedIr9vcT/JfSwDi9IARP7d6RnhG5m8T4pCx+hyUfC7rc0854P+6pu/pGy/iVVVFXDypGIBVoD2dtPqK/BAoGI5M0cJaAiFTOuwFnYJrabGgN9Ppg/Zn5PzZdIp0sGwVOnzCUujjxDwigpmm7KxHQ7TccgSnozEQiGWtCorYTFh8Hv4/cI+rPiQk+OQgBmWBEMhfg85D9bQYKKjw8D27UbJMHwRHCB7bDLDKBRgO5hVqwxrYJzINVWlHA1ppAzrIGevs6bGRDjMQeaqKrKo8NDl0ykPG3loyNkoWfKRka7s6/G+8TOrq3nf5GEiI2KWfWGBEAhgkIeN/J6GgSWlxNISH1A8IOXMmWQDkuAFw+CBK502ABugVFrqpAmrFwQb6SrvGfloi99BZsjygAJkuVCz9638uVwvoLhmcq1kOU8GNqVZl7x2l6uYWcqfy0xCsr3IfZXLcd1lMCK/j5wxJfl1ca3k58o9WnpAv57J7yUPall6lGVMmSFJR1uaMUmTgZRcD+4RYXHPFp0BS7Sm7eDkvZHZoHRSMkiU+wQoZuZvZqUOTa61DGrl/ZBBB1DcL3IPyOCvNGuXRAhyvlWWmmW2TxBb8fmR908GGjL7dLlgjZcJO2iX6yAD0l/FpJOn/qqBhgbiCA4e1N78j39FK2eM59nS6Vns2hW2NxUjamH3LSoq6ARIaVV8QAGODTgcZCfp7OQ8EZGgBO1kMtwkEm4vGfxl2a1Q4EHT0cE5Oz5sChob+b5Sc469CsUiKBfWgcs+oCwbBgJ0fD4fHaGmKZZgK7+ny0Vqs+pqgfe9z8D4uIpwmCCFcJgzbhLAACw9oKidVxTEbWjg3J+uC2QyKi64gDyRfD+OFTidGtasEbYah5Qi4kyYYmWRMvNU7ChfHoheL6+hvp4oTL+faNZCgcCQigphwcwV1NWZcDpZdm5qMq2h5GLPS6q+AwQqySCnslJYKiFKSXlKsQ90mZXJQ1Q6RoejiCgMBIR10Cj2EDq1/Rg4GQaZcAoF1Sqrs+fjcBSDo9JDTBJ5A3Q2LpdiOydFMeB28/vLDKfUccoDTwZXZCdhjyyTKYKHpCOvqJAMLYoN8gKK/VfOxMHm7TRNcsMqCuzhbqeT98jtJtDL4yFXq8sl7LUodfoyWDjdqchrk9mOzF5kad/n416VTk06Jip5FMVz6TTkXjet4f8i1V5pgOt0CnuMQd4DBmXFzyrNYKurhQ1yCQaFnemfnrXLNZQBVem9kQ5WlrElklgGS6UBj8ya/X72iOXvKiuLlR2Se/D+yrWSZ4u8pkCg+PkyUKAguPwMUgIqimKX0EurAKUBwdlMroF8fwaiBVxwAVs5fv+5n2UsO8bzbNnsLISoQV8fDxyvlwe/zwc0NXGWT87UJRKKNavGA16yr2zbZmJmhgCFZcuExUghrD4Ty4kslfDQrakRdiba2Wla0SEZIjweOmSvlyMCkmF/cVH2IDmwm0yqqKw0bCLqujoy30gUqaSkMgwFHR0GOjroXK66imLHySSBFrLXuGYNadKosSbsw1dmA6oq7PJsXZ3MLBWsWlVAKqXY/ceODhOqmkIq5bZYV+j46+uBmhqpaK9a81V09Om0ah+c8gHz+fh54TBp8JgFEnAkD5LaWtOCuTOYqasjYbPMRDkGIKy+MGf5LrusgKoq2Ajiykre71WrCHaQpZ9CQaqCFA/s0izB4eABTFV2xc7k43GW2CmJxGtgfxNWaVxY4sM8iOJxViDWrzewsMAeqM9HkFR1tWIf0sya8qiq0qxeIE/JYnZAJXWZ5QSDAitXmrYElBC8P8EgD3q/n9/b42HGX1nJXnFDgwFNY3bl83EshdmjYpN0c16PlYBAgOsgqyA8nLlPQyFhH4zBIFBVZWBhoVgEk4d1qVMCiuVnVgqE5VwUbN6cB8Dv7fMxGGlqMpDPFyneKio4S1xRUZRDMwxeA9sgXJuWFvZ3MxkDiqIhEGDVJhTis6Uo3PcXX1yweqfCIplnMKXrxTGi0ixcmt8v15z7Qzr5ysoi6XdlpbADXPbliNyWZ05jI+epWXniXqyoKJJDBAKwRJ1hSUAZWLlS2HOURLrzPudyXCO5xn4/nx/ZfqmqIglAQ4NhAXRkBUCClJaW4uU1y2pOaaUnFBJYtSqOUMiFUk7mc2ll8M15tr6+Pni9q/DjHzsQicAi3iayUSrAP/+8jnDYQFcXHcr+/RqSSR5+XV2MjkdHVTtjCoeFzUP6zDMkBujr4yiAw8EHqaJCYMsWE0Kwh1hfz6H1dJroutZW0zpU+XOHgxylU1OKrWYxM6PgwAHq661ebaCigvyQ+TyzAalusWwZAT+zs/xbv5/jJa+8omPLloLFIgIcPcrZzVOnOI4RCAibP9blAjZtKmBxkYTItbUc3Ha5SNG1erVhsW0IxGILWLUqgKkpUpfNzano7DTg8wHHj3NkRGZ8kYiChoYCnE6ysExPa6ioIFNQXR3n2IJB085+5uZIEBCPc/1Ja0XBV49HYPNmjsQ8+qjDeg2zhpkZ9jUvusi0ZYSOHNHs++pyca6zv58qIB4PEascLxEWUKooGaUoHBpPp4G2tgJCIY7HTE3RQSSTKlauZEBy+vXJdZid1dDaalhE8DwUR0ZIQkA1DSJHhZAzjwnU1flswIauc3RFShxVVrJqIZGJXi8Pv+Fh1SKfYMAWj9O5DQ2pWLuW13jiBE/MbdsMJJPAs8/qCIfJ+TowQNTzhg3c00NDqp2xVlYKKxAzEYloCIdJmh0Omzb7zuQkWYeamwVOnCCV3Py8amVNwlLlYDBYVcUsX1GYyS4ssMrh9zPwkYGfz8dWwLFjpAisrz99vwDZrMDcnAa/n1SJpLGjSPCyZUSLjo4mccstLkxPq3jpJd57n4/XUrrH9u8ndy01QEli7naz0rKwwNENmalVVvL8qK01MD1NYBm1MenMKysFamsNTE1R0SSZ5HU5HAo2by5gwwYiYiUobWpKsVC7dGwjIxzn2LzZQF2dsICBJhIJ7qNwmEF5fz+BXrOzquXwFPT0cDRrwwYDXi8VYJJJovA7OgT6+jhDKffFyy8r2LfPAZ+PDn1hgUj9igoyKAGwviOrBYGADPbnAVCD9b//93PPl1p2jOfZpKDm+PhSJhspoAvgrL8D8LqvP5u9lfeXorey3FRZSa7WN7qWhgYTR49qOHSIUe4FF5jYvr1w1ms5299OTqpv+Tu/0TVMTtJhUmw1gm3bQvbf/KprW3rtr/f7N7snCwsKjh8HRkfJU3vFFQZuvDH/pu8NMJg5dIgHBsCI3+8XZ13bX/f6Xu+1qkrU87FjnGOsqRG49FID27cXkEqdtPfq0msEAGbVFRWw5mE5g+j3kx1Izo3GYsV9VXr/S0Etp+8NeU3Dw8qSPSa/j7zv0k7fv2f7nLk5OgOJVt6w4ez79vRnIxiEhdbGkiD0re6n/ftV7NihY2JCRWOjiQsvHMCHPtSMs9nZ7svICMv+ra1c04EBKnxkMmxd1NXRWci1f6vP4+nrX7pf+vpIAReP8/5Ktid5LXLtfpX9VXov1641lpwDp58Lui6we7dmr9m2bXT4p585pc/OwsIirrzS/7rf/Te1smM8z3a+laZ/H628pufHyut67q28pufHzve6llGpZStb2cpWtrKVWNkxlq1sZStb2cpWYmXHWLayla1sZStbiZUdY9nKVrayla1sJVYG35StbGUrW9nKVmLljLFsZStb2cpWthIrO8ayla1sZStb2Uqs7BjLVrayla1sZSuxsmMsW9nKVrayla3Eyo6xbGUrW9nKVrYSKzvG82jf+973sH79etTV1eHKK6/Eyy+//HZf0jvS7rnnHgSDwSX/rFq1yv69EAL33HMPOjs7UV9fj/e///3o6elZ8h6xWAy33norWlpa0NLSgltvvRWxUoLN3wN76aWX8LGPfQxr1qxBMBjEj370oyW/P1freOzYMfzhH/4h6uvrsWbNGnz961+HeKtqs+8ye7M1/eQnP3nG3t2+ffuS12SzWXzhC1/AihUr0NjYiI997GMYHx9f8prR0VHcfPPNaGxsxIoVK/BXf/VXyL1VpeJ3mf3DP/wDrrrqKixbtgzt7e24+eabcfz48SWvebv3atkxnid77LHH8MUvfhGf//zn8fzzz2PLli248cYbMTo6+nZf2jvSOjo60Nvba/9TGkR8+9vfxne/+118/etfx86dOxEOh/HhD38Y8Xjcfs0tt9yCw4cP45FHHsGjjz6Kw4cP4xOf+MTb8VXeNksmk+jq6sLXvvY1eE5X18W5WcfFxUV8+MMfRm1tLXbu3Imvfe1r+Kd/+ifce++9v5Xv+Nu2N1tTAHjPe96zZO8+8sgjS37/pS99CY8//jgeeOABPPnkk4jH47j55pthWDpShmHg5ptvRiKRwJNPPokHHngAO3bswB133HHev9/bYS+++CI+/vGP4+mnn8aOHTug6zo+9KEPYX5+3n7N271Xy3OM58muueYadHd34zvf+Y79s40bN+KDH/wg/vf//t9v45W98+yee+7Bjh07sHv37jN+J4RAZ2cn/uIv/gK33347ACCdTqOjowN/93d/hz/7sz9Db28vLr74Yjz11FPYunUrAGD37t247rrrsG/fvt9LEuempib8/d//Pf74j/8YwLlbxwceeAB/+7d/i5MnT9qO4hvf+Aa+//3v4/jx41Ck2u3voJ2+pgAzxrm5OTz88MNn/ZuFhQWsXLkS3/3ud3HTTTcBAMbGxrBu3To8+uijuOaaa/CLX/wCN910E44cOYLmZipxPPzww7jtttvQ19eHQCBw/r/c22iJRAItLS340Y9+hOuuu+4dsVfLGeN5sFwuh0OHDuHqq69e8vOrr74ar7zyytt0Ve9sGxoawpo1a7B+/Xr8+Z//OYaGhgAAw8PDmJ6eXrKWHo8Hl1xyib2We/fuhd/vx8UXX2y/ZuvWrfD5fOX1tuxcrePevXuxbdu2JdnTNddcg8nJSQwPD/+Wvs07y3bv3o2VK1di06ZNuO222zA7O2v/7tChQ8jn80vWvbm5GatXr16ypqtXr7adIsA1zWazOHTo0G/vi7xNlkgkYJomgsEggHfGXi07xvNg0WgUhmEgHA4v+Xk4HMbMzMzbdFXvXNu8eTPuu+8+PPLII/jOd76D6elpXHvttZibm8P09DQAvOFazszMoLq6ekkEqCgKampqyutt2blax5mZmbO+h/zd75tt374d//zP/4z//M//xFe/+lXs378fN9xwA7LZLACuiaZpqK6uXvJ3p6/76WtaXV0NTdN+L9b0i1/8ItatW4ctW7YAeGfsVf03+D5lexM7PVUXQvxOl5p+XXvve9+75P83b96MDRs24KGHHsJFF10E4M3X8mzrWl7vM+1crOPZ3uP1/vZbCbiFAAAJlUlEQVR33T760Y/a/93d3Y0NGzZg3bp1ePrpp3HDDTe87t+9lXV/o5//rtiXv/xl7NmzB0899RQ0TVvyu7dzr5YzxvNgrxftRSKRMyKYsp1pfr8fnZ2dGBgYQF1dHYAzI7zStaytrUUkElmCNhNCIBqNltfbsnO1jrW1tWd9D+DMCP/30RoaGtDY2IiBgQEAXC/DMBCNRpe87vR1P31NX6/q9LtkX/rSl/CTn/wEO3bsQFtbm/3zd8JeLTvG82BOpxMbNmzArl27lvx8165dS2riZTu7ZTIZ9PX1oa6uDq2trairq1uylplMBrt377bXcsuWLUgkEti7d6/9mr179yKZTJbX27JztY5btmzB7t27kclk7Nfs2rULDQ0NaG1t/S19m3euRaNRTE5O2of7hg0b4HA4lqz7+Pi4DR4BuKa9vb1LRjh27doFl8uFDRs2/Ha/wG/J/vqv/xqPPvooduzYsWQ0C3hn7FXti1/84t/+pl+ybGdaRUUF7rnnHtTX18PtduMb3/gGXn75Zdx7772orKx8uy/vHWV/8zd/A6fTCdM0cerUKXzhC1/AwMAAvvWtbyEYDMIwDHzrW9/CypUrYRgG7rjjDkxPT+Mf//Ef4XK5UFNTg1dffRWPPvoo1q9fj/HxcXzuc5/Dxo0bf69GNhKJBE6cOIHp6Wn827/9G7q6uhAIBJDL5VBZWXlO1rG9vR0/+MEPcOTIEXR0dGD37t2488478dnPfvZ3Mgh5ozXVNA1f+cpX4Pf7USgUcOTIEXzmM5+BYRj4xje+AZfLBbfbjampKfzLv/wL1q5di4WFBXzuc59DIBDAXXfdBVVV0dbWhscffxw7d+5Ed3c3Tpw4gdtvvx033ngjPvCBD7zdS3DO7fbbb8e///u/41//9V/R3NyMZDKJZDIJgEmFoihv+14tj2ucR/ve976Hb3/725iensaaNWv+f3t3G9JU+8cB/Lv1YK2iFemWkCCSSc2enBBGYjWyWnvjKTTNsLAMCspardIiSXSJFSQqDCozhLLUqCCG9sAE06DoYWJYQj4gGQ2dRUnRzv9FOM7cursR7zb7fz+wN+e6uHbt4Pxyruvs/FBQUICVK1f6e1oBZ+fOnWhqaoLD4cCcOXOg1WqRk5ODqKgoAD+XSMxmMyoqKjAwMICYmBgUFxdj4cKF7jH6+/thMplw7949AMCGDRtQVFTkvtPt/0FjY6PPf6Rbt25FeXn5mJ3H1tZWGI1GPHv2DEqlEjt27IDJZPor98P+6ZyeO3cOaWlpePnyJZxOJ1QqFVatWoWcnByPO0yHhoZw4sQJ3Lx5E0NDQ4iPj8fZs2c9+nR3d8NoNMJms2HKlCnYvHkz8vPzERQU9Ec+55/0q++kyWTCsWPHAIzdd360f6sMRiIiIgnuMRIREUkwGImIiCQYjERERBIMRiIiIgkGIxERkQSDkYiISILBSORnVVVVUCqV46o6hV6vh16v9/c0iP4TDEaiAHP9+nWUlZX5exp4/PgxCgsLvaqiE/3t+AN/Ij/78eMHvn//jqCgIMhkMgiCgPb2drx69cqv8zp//jzy8vLw4sULr2dLfvv2DcDPR3gR/W1YdorIzyZMmOBVcue/8OXLFygUijEZi4FIfzMupRL5mXSPUa/X4/79++ju7oZSqXS/homiCIvFgri4OKhUKoSHh2PXrl0elRmAn3uAsbGxsNvtMBgMCA0NxaFDhwAATU1NyMjIgEajQUhICKKionDgwAGPJdPCwkLk5eUBAJYsWeKeR2Njo3v8kXuMX79+xalTpxAdHY2QkBAsXrwY+fn57qK9w6KjoyEIAp4+fYr169dDrVZj0aJFAbF8TATwipEooBiNRgwMDOD9+/coKCjwaj948CAqKyuRnJyMzMxM9PX1wWKxoKWlBTabzSNEnU4nkpKSYDAYIAiCu6pLXV0d+vv7sX37dqhUKtjtdlRWVqKtrQ1WqxUAYDAY8ObNG9TW1qKgoMBdgX7BggU+5y2KItLT09HQ0ICUlBRotVo0NzejuLgYbW1tqKqq8ujf2dmJlJQUpKamYsuWLaitrcXx48cRFRWFNWvWjMm5JBotBiNRAFm9ejXUajUGBweRnJzs0dbS0oLLly+jtLQUaWlp7uMGgwEJCQmwWCw4cuSI+/iHDx9gNpuxZ88ej3Hy8vK8llS1Wi2ysrLQ3NyMFStWQKPRIDo6GrW1tdDr9b+tX2e1WtHQ0ACj0Yjc3FwAQGZmJoKDg1FeXo5Hjx4hISHB3f/t27e4deuW+9i2bdug0Whw5coVBiP5HZdSicaJuro6TJ8+HevWrYPD4XC/5s6di4iICNhsNo/+EydOREZGhtc4w6EoiiIGBwfhcDjc9emeP38+qrlZrVbIZDLs27fP4/j+/fvd7VIREREeQRkUFAStVot3796N6v2JxhKvGInGiY6ODnz+/Bnz58/32T6yxtxwkeyRenp6cPLkSdTX1+PTp08ebU6nc1Rz6+rqgkql8qq1p1arMXPmTHR1dXkcnzdvntcYSqUSra2to3p/orHEYCQaJ1wuF2bPno1Lly75bB+5PDp16lSfYyQlJeHjx4/Izs5GZGQkpk2bBpfLBUEQ4HK5xnzeouj9i7Bf3YXrqy/Rn8ZgJAowv6ouHh4ejocPHyImJgYzZswY1dh2ux3t7e0oKytDamqq+3hHR8e/nocvYWFhePDgAQYGBjyuGvv6+jA4OIiwsLBRzZfIH7jHSBRgFAqFzyXNpKQkuFwumM1mrzZRFOFwOH479vCV2sgrs5KSEp/zAPCvnnyTmJgIURS9fnJx4cIFdzvReMErRqIAs2zZMty+fRsmkwlarRZyuRyCICAuLg5ZWVkoLS2F3W6HTqeDQqFAZ2cn7t69i/T0dGRnZ//j2JGRkYiIiEBubi56e3sxa9Ys1NfXo7e31+c8AOD06dMQBAGTJ09GfHw8goODvfomJiZCp9OhqKgIPT09WL58OZ48eYLq6mps3LjR40YbokDHYCQKMLt378br169RXV0Ni8UCURQhCAIA4MyZM1i6dCkuXryIwsJCyOVyhIaGYu3atdi0adNvx540aRKuXbuGo0ePoqSkBHK5HDqdDjU1NYiMjPToGxsbi9zcXFRUVGDv3r1wuVy4c+eOz2CUyWS4evUqzGYzampqcOPGDajVahiNRhw+fHhsTgzRH8JnpRIREUlwj5GIiEiCwUhERCTBYCQiIpJgMBIREUkwGImIiCQYjERERBIMRiIiIgkGIxERkQSDkYiISILBSEREJPE/oOyPye2d3pkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18536360b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort with best loss first\n",
    "tpe_results = tpe_results.sort_values('loss', ascending = True).reset_index()\n",
    "\n",
    "plt.plot(tpe_results['iteration'], tpe_results['loss'], 'bo', alpha = 0.3);\n",
    "plt.xlabel('iteration'); plt.ylabel('loss'); plt.title('TPE Sequence of Losses');\n",
    "\n",
    "print('Best Loss of {:.4f} occured at iteration {}'.format(tpe_results['loss'][0], tpe_results['iteration'][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### Random Results\n",
    "\n",
    "We should contrast the TPE results with those from random search. Here we do not expect to see any trend in values evaluated over time because the values are selected randomly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>iteration</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36.210073</td>\n",
       "      <td>0</td>\n",
       "      <td>5.957885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-202.384052</td>\n",
       "      <td>1</td>\n",
       "      <td>4.470885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-75.519449</td>\n",
       "      <td>2</td>\n",
       "      <td>3.218963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.543552</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.515859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.078011</td>\n",
       "      <td>4</td>\n",
       "      <td>-4.916832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss  iteration         x\n",
       "0   36.210073          0  5.957885\n",
       "1 -202.384052          1  4.470885\n",
       "2  -75.519449          2  3.218963\n",
       "3    5.543552          3 -0.515859\n",
       "4   35.078011          4 -4.916832"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_results = pd.DataFrame({'loss': [x['loss'] for x in rand_trials.results], 'iteration': rand_trials.idxs_vals[0]['x'],\n",
    "                            'x': rand_trials.idxs_vals[1]['x']})\n",
    "                            \n",
    "rand_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1853838cf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 8))\n",
    "plt.plot(rand_results['iteration'], rand_results['x'],  'bo', alpha = 0.5);\n",
    "plt.xlabel('Iteration', size = 22); plt.ylabel('x value', size = 22); plt.title('Random Sequence of Values', size = 24);\n",
    "plt.hlines(minx, 0, 2000, linestyles = '--', colors = 'r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Clearly, the random algorithm is just \"randomly\" choosing the next set of values to try! There is no logical order here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Loss of -219.8012 occured at iteration 235\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18538855320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort with best loss first\n",
    "rand_results = rand_results.sort_values('loss', ascending = True).reset_index()\n",
    "\n",
    "plt.figure(figsize = (8, 6))\n",
    "plt.hist(rand_results['x'], bins = 50, edgecolor = 'k');\n",
    "plt.title('Histogram of Random Values'); plt.xlabel('Value of x'); plt.ylabel('Count');\n",
    "\n",
    "# Print information\n",
    "print('Best Loss of {:.4f} occured at iteration {}'.format(rand_results['loss'][0], rand_results['iteration'][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Again, there is no discernable clustering because this method is choosing the next values randomly. In this case, it happended across the minimum of the function because it was a relatively simple problem and we used many iterations. This would not likely be the case when we are optimizing more complex functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Slightly Advanced Concepts\n",
    "\n",
    "These probably should not be called advanced but smarter concepts because they will make our jobs easier. We will cover two upgrades we can make:\n",
    "\n",
    "* Smarter domain space over which to search\n",
    "* Return more useful information from the objective function\n",
    "\n",
    "These will be helpful when it comes time for hyperparameter optimization. Master the details on the easy problems so you can use the tools more effectively on the difficult problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### Better Domain Space\n",
    "\n",
    "In this problem, we can cheat because we know where the minimum is and therefore can define a region of higher probability around this value of x. In more complicated problems, we don't have a graph to show us the minimum, but we can still use experience and knowledge to inform our choice of a domain space. \n",
    "\n",
    "Here we will make a normally distributed domain space around the value where the minimum of the objective function occurs, around 4.9. This is simple to do in Hyperopt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "# Normally distributed space\n",
    "space = hp.normal('x', 4.9, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x185388171d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = []\n",
    "\n",
    "# Sample 10000 values from the range\n",
    "for _ in range(10000):\n",
    "    samples.append(sample(space))\n",
    "    \n",
    "\n",
    "# Histogram of the values\n",
    "plt.hist(samples, bins = 20, edgecolor = 'black'); \n",
    "plt.xlabel('x'); plt.ylabel('Frequency'); plt.title('Domain Space');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Much closer to the true value! This would help both the random search and the TPE find the minimum quicker (for random search it would help because we are concentrating the possible values around the optimum)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## More Useful Trials Object\n",
    "\n",
    "Another modification to make is to return more useful information from the objective function. We do this using a dictionary with any information we want included. The only requirements are that the dictionary must contain a single real-valued metric to minimize stored under a `\"loss\"` key and whether the function sucessfully ran, stored under a `\"status\"` key. Here we make the modifications to the objective to store the value of x as well as the time to evaluate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "from hyperopt import STATUS_OK\n",
    "from timeit import default_timer as timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "def objective(x):\n",
    "    \"\"\"Objective function to minimize with smarter return values\"\"\"\n",
    "    \n",
    "    # Create the polynomial object\n",
    "    f = np.poly1d([1, -2, -28, 28, 12, -26, 100])\n",
    "\n",
    "    # Evaluate the function\n",
    "    start = timer()\n",
    "    loss = f(x) * 0.05\n",
    "    end = timer()\n",
    "    \n",
    "    # Calculate time to evaluate\n",
    "    time_elapsed = end - start\n",
    "    \n",
    "    results = {'loss': loss, 'status': STATUS_OK, 'x': x, 'time': time_elapsed}\n",
    "    \n",
    "    # Return dictionary\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "We run the algorithm the same as before. We'll create a new `Trials` object to save new results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "# New trials object\n",
    "trials = Trials()\n",
    "\n",
    "# Run 2000 evals with the tpe algorithm\n",
    "best = fmin(fn=objective, space=space, algo=tpe_algo, trials=trials, \n",
    "                max_evals=2000, rstate= np.random.RandomState(120))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "This time our trials object will have all of our information in the `results` attribute. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'loss': -189.3682041842684,\n",
       "  'status': 'ok',\n",
       "  'time': 3.925334449839113e-05,\n",
       "  'x': 5.312379584994148},\n",
       " {'loss': -219.325099632915,\n",
       "  'status': 'ok',\n",
       "  'time': 2.503111823060067e-05,\n",
       "  'x': 4.8166084516702705}]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = trials.results\n",
    "results[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "This time we have more information from the results. We can extract the results into a dataframe for inspect and plotting if we like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>loss</th>\n",
       "      <th>x</th>\n",
       "      <th>iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>0.000030</td>\n",
       "      <td>-219.801204</td>\n",
       "      <td>4.878152</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>0.000029</td>\n",
       "      <td>-219.801204</td>\n",
       "      <td>4.878111</td>\n",
       "      <td>1316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>0.000027</td>\n",
       "      <td>-219.801204</td>\n",
       "      <td>4.878189</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>0.000022</td>\n",
       "      <td>-219.801203</td>\n",
       "      <td>4.878064</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>0.000031</td>\n",
       "      <td>-219.801203</td>\n",
       "      <td>4.878222</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          time        loss         x  iteration\n",
       "956   0.000030 -219.801204  4.878152        956\n",
       "1316  0.000029 -219.801204  4.878111       1316\n",
       "402   0.000027 -219.801204  4.878189        402\n",
       "914   0.000022 -219.801203  4.878064        914\n",
       "1954  0.000031 -219.801203  4.878222       1954"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Results into a dataframe\n",
    "results_df = pd.DataFrame({'time': [x['time'] for x in results], \n",
    "                           'loss': [x['loss'] for x in results],\n",
    "                           'x': [x['x'] for x in results],\n",
    "                            'iteration': list(range(len(results)))})\n",
    "\n",
    "# Sort with lowest loss on top\n",
    "results_df = results_df.sort_values('loss', ascending = True)\n",
    "results_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "This was a simple optimization problem, so the lowest score obtained does not really differ from that with the uniform distribution. It took around the same number of iterations to converge. We can compare the distribution though to see if better values were tried."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18536e9a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(results_df['x'], bins = 50, edgecolor = 'k');\n",
    "plt.title('Histogram of TPE Values'); plt.xlabel('Value of x'); plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Indeed the values of x evaluated cluster much closer to the optimum! The algorithm spend much more time around the best value than searching the domain space. We can compare this distribution to that attained with the TPE algorithm on the uniform domain using a Kernel Density Estimate Plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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44osv4ObmJkmCYuLnsWPHjjLH+fzzz2FiYoIHDx4gPz9fKkECkPSiK0/8xVVZO0t54ufG09NT7ge6U6dO2LhxoyRGdSr/flT0XjM1NYWrqysuX76M1NRUuLq6Sq1X1Xutpu8beRwcHGReS+DDa5ebmwtDQ0OFx6jp57a86r5/FD3/AODj44OrV6/i2rVrlbbxA8A///wDAOjWrVuVsV28eBFAWSlPXmlB3Pv73r17VR6rJgYOHIh58+bBw8MDffv2Rfv27eHh4aF0pxNlXuvr16+Dw+HA09NTZntFz6uqnT9/HufPnwdQ9oPb3NwcQ4YMwbfffgsXFxeZ7Tt06ICYmJgan08tic7c3Bz37t3D06dPldovLy8PACqtNhAX98XblSfvQyz+xaNonbxf7JXFoKurC0NDQ6nz5+TkoHPnzkhPT0ebNm0wePBgNGzYEDweD7m5ufj5558rbXtTpnrE2toap0+fxsqVKxEXFyd5E5iammLcuHGYMWOG5Jry8vJgZGSE+vXryz2WmZkZMjMzkZeXJ/OBkdc2Jj6uvA4SFdXmNWSKOGGU/9XM9nutNu8beSobi6rMayf+wlX2c1tVHPJiUNX7RNzlvTqlUHHns6NHj8r08iuv4g8OVZk0aRJMTEywbds2REZGYsuWLQCAL774AgsXLpT7w1QeZV7rt2/fwsjISG57qjrHl4rH1amLWhKdl5cXEhIScO7cOUmng+oQv4CvXr2Su15ctaKOAeavXr2S6fRSWFiIt2/fomHDhpJlv/32G9LT0+W+kJcuXVLYgUPZX85OTk6IjIyEQCDArVu3kJCQgMjISISFhUEoFEqGahgZGeHNmzd4//693GTH5PP4Mb2GYuKenuXbu8rHKW/gKdNx1uZ9wxQvLy8AZc+XQCCQqhpTNVW9T8Q/zJ4/f15lxxXxsXbt2oXevXsrFW9F4lqUyn5AVBxzJjZgwAAMGDAAeXl5+Pvvv3Hy5Ens3LkTAwYMQFJSEhwdHWsVV0WGhobIzc1FUVGRTLKr7LnXBGppo/vmm2+gra2No0eP4vbt2wq3Lf+rVfxGldd9uKioSFL1UJ2eWLUlLmaXd+HCBYhEIqmqK3F1h7wPjrxjqAKPx4O7uzumTJki6TV6/PhxyXrx81OxKz8A3L59G5mZmXB0dJRb/VFb4ufm4sWLcocxiLtwy6viYsLZs2eRkpICPT099OzZU7Jc0Xvt9evXuHPnjmQmECaw8b6pSocOHdCsWTM8e/asWsMSlB1yUp6i5x8oG2IEVP0+Ef94iY2NrfKc4m3FnW5qQ/zjqHxnF7HS0lKZ5o2KjIyM0KVLF6xevRpTpkxBYWEh4uPjax1XRe7u7hCJREhJSZFZJ/4+1URqSXQ2Njb47rvvUFJSgoEDB+Lvv/+Wu11KSopUA/LAgQOho6ODbdu2ydSTR0RE4NmzZ+jWrZtU5wGmrF69WqpN4f379wgLCwMAqR4/NjY2AGQ/sNeuXcMPP/ygsnguX74sdxyYeFn5hmZx9+ilS5ciPz9fsrykpATfffcdAChV0laGlZUVunTpgqdPn2LdunVS6+7cuYPt27ejXr16GDhwICPnFxOJRDh8+DBGjhwJoKwxvnxVjbiDREREhNTzKhKJsHDhQrx79w5DhgxhbFYKdb1vlMHlcvHjjz9CW1sboaGhiIqKkrTblZeeno6RI0dW2VFEkYCAADRq1AhHjhyRSexRUVG4cuUKPvvssyp7nQ4dOhRGRkbYuXMnzpw5I7O+fDVsjx49YG9vjx07dsgMdRC7du2a3PG1FRkaGsLFxQUXL17ErVu3JMtFIhFWrFghNwHGxcXJbSqR9xlWlcGDBwMAli9fLvXDJC8vT26PYk2htplRpk+fjtLSUixfvhxfffUV2rZti9atW8PQ0BBZWVm4dOkSbt++LdVuYmNjg5UrVyI4OBidO3dGYGAgzMzMcPHiRZw/fx5WVlYyAyuZ4uzsDC8vL/Tu3Vsyji4tLQ09evSQGlowePBgrF+/HvPmzUNSUhIcHBzw4MEDnDp1Cr169UJ0dLRK4jlw4AAiIyPh5eUFBwcHNGrUCE+ePMGJEyfA5XKlhgp8/fXXOHnyJA4cOABPT08EBARIxtHdv38fPj4+lQ50VYWIiAh0794dy5YtQ0JCAr744gvJOLr3799j3bp1Ur3haismJgaPHz8GUPaD5Pnz50hOTkZGRgZ0dXURFhaGKVOmSO3Trl07BAcHIyIiAl5eXggMDISRkRHOnDmDa9euwdXVFQsWLFBZjBWp632jrPbt2yMqKgrjxo3D5MmTsWbNGnTs2BEmJibIz8/HjRs3cOnSJXC5XMyYMaPG59HX18emTZswfPhwBAYGonfv3rC1tcXNmzcRGxuLBg0aYPPmzVVW7zdq1Ajbt2/H8OHD0a9fP3Tu3BktWrRAQUEB7t27h8TEREnXfW1tbfz+++/o168fhg4dirZt26JFixbQ19fH06dPcf36daSmpiIhIQGNGjWq8hpmzJiB8ePHw9/fH4GBgdDT08PFixfx9OlTeHt7y9SoBAUFQUdHB15eXrCxsQGHw5EMx7C1tZX0BFelIUOGIDo6GvHx8fDy8kKPHj1QWlqKY8eOoUWLFrh7965MZ7aPgaLhBQAwevRohbMoqXVS51mzZiEwMBCRkZFISEjA3r178e7dO/D5fLi6umLlypUy85mNGjUK9vb22LBhA2JiYlBQUAALCwuMGzdOpls3k3bs2IFVq1Zh//79ePnyJSwsLBAaGooZM2ZIffgsLCzw559/YvHixUhJScHp06fh5OSEtWvXwsfHR2VfWP3790dJSQkuXryII0eO4N27dzAzM4Ofnx8mT54sNXwCALZs2YL27dvjt99+w2+//QahUAgHBwcsXboUEyZMYHT+vKZNm+Ls2bNYs2YNTp48iZSUFOjr66NDhw6YNm1atRvdq+vEiRM4ceIEOByOZFJnNzc3jB8/HgMHDqz0A7Fw4UK4u7tj69atOHDgAIqKitC0aVPMmjUL3377bZW9FGtDXe+bmujWrRuuXLmC7du3Iz4+HsePH0deXh709PTg4OCA6dOn43//+1+t57Ts3r07YmNjERERgXPnzuHIkSMwMTHBkCFDMHv27GpPGt21a1ecPXsWP/74I86dO4fExEQYGhrC3t4e8+bNk9rW1dUV58+fx+bNm3HixAns2bMHIpEIZmZmcHFxwdSpU6tdXT1o0CCIRCKsX78ee/fuhYGBAXx9ffHbb79h2bJlMtsvXrwYp0+fxo0bN/DXX39BS0sLTZo0wZw5czB+/HiFkxTXFIfDwe+//461a9di37592Lp1K8zMzDB48GAEBQXhxIkTH+Wk+uLhBZUJCAhQmOjUOqlzXSSepbs6XekJIaSuOnPmDPr27Yv+/fsrNQtPXfDxlVEJIYQw5sWLFzLLsrOzsXjxYgDyO0TVdWq/Hx0hhBD2LFy4EFevXkW7du3QuHFjPHv2DHFxcXjz5g169OhRrfu/1TWU6Agh5BMSEBCAzMxMxMfHIzs7G9ra2mjWrBlmz56NsWPH1momnI8VtdERQgjRaNRGRwghRKNRoiOEEKLRKNERQgjRaBqR6FJTU9kOgVGafH10bXWTJl8boNnXp8nXVhmNSHSEEEJIZSjREUII0WiU6AghhGg0SnSEEEI0GmszowgEAoSHh0vuBmBmZoaBAwdi7ty50NJSPqyCggKUlpYyECn7dHV1K71DcV2nidemr69fo/cwIYQZrH0af/zxR0RGRmLz5s1wdXXFrVu3MHHiROjo6GD27NlKHUs8ZU2DBg2YCJV19erVY+QmjB8DTbs2kUiEnJwcRm/pQwhRDmuJ7tKlS+jevTv8/f0BlN2zzN/fH5cvX1b6WNra2tDT01N1iIQojcPhgM/nIy8vj+1QyEfoSNp7bLyZD3sjHsI9+GhYj1qP1IG1Z9nT0xNJSUm4d+8eAOC///5DYmIivvrqK6WPxeVyNXIiUlI30XuRyJNVKMDos9m4lFmMvQ/e44frb9kO6ZPB2qTOIpEIYWFhiIiIAI/HQ2lpKWbNmoX58+cr3E/eYEddXV2YmJgwFSohSsvMzERhYSHbYZCPyK4MLWxI05Fa9rf3O5ai+ThU9+7ttcVa1WV0dDT27t2LyMhIuLi44MaNG5g7dy5sbGwwfPjwSveT98Q8efJEo9p5KiosLNTY69PUazMyMkJhYaHaPsjqlpqaqrHXBjBzfQYFeUCadCmOjedQ0187eViruly4cCGmTJmCr7/+Gm5ubhg8eDAmT56MH374ga2QSDVNnDgRgwYNYjuMWgsPD4eXlxfbYZBPhDaXqrTZwlqie/fuHXg8ntQyHo8HoVDIUkTqNXHiRPD5fKxevVpqeWJiIvh8PrKysliKrPbS09PB5/Mlf1ZWVmjbti2mTp2Kmzdvsh2exNSpUxETE8N2GOQToU15jjWsJbru3bvjxx9/xKlTp5Ceno5jx45h48aN6NmzJ1shqZ2uri7Wr1+P169fq/S4xcXFKj1eTR06dAh3797F+fPnsXz5cmRmZqJTp044dOgQ26EBAAwMDNCoUSO2wyCfCG0eZTq2sJboVq1ahd69e2PmzJnw8PDA/PnzMWLECCxYsICtkNSuY8eOsLa2xqpVqxRul5ycjC5dusDMzAxOTk4IDQ2VSmYBAQEIDg7G/Pnz4eDgAD8/PwAAn8/Htm3bMGTIEFhYWKBNmzZISEjA06dP0a9fP1haWsLb2xtXr16VHCs7OxtBQUFwdXWFubk5PD098fvvv9fo+ho1agQzMzPY2tqiW7du2Lt3LwIDAzFjxgzk5ORItjt69Cjat28PU1NTuLm5Yc2aNRCJPvSRat68OVauXImJEyeiSZMmcHNzQ3R0NHJycjB69GhYWVmhdevWOH36tGQfgUCAKVOmwN3dHebm5mjdujXWrVsnVWNQsepSXCW7efNmfPbZZ2jatCkmTZqEd+8+7Q4DRDV0qOqSNax1RjE0NMSKFSuwYsUKRo7P3/GUkeNWJmeUldL7cLlcLF68GN988w0mTpwIOzs7mW2ePXuGoUOHYvDgwdi0aRMePXqEadOmgcvlYtmyZZLt9u/fjxEjRuDPP/+UShJr1qzB999/j7CwMISHh2PMmDH4/PPPERQUhFWrViE0NBSTJk3ChQsXAJR1DmnRogW+/fZbGBkZ4ezZs5gxYwasra3h4+NTg2dG2pQpU3Dw4EGcO3cOffr0wbVr1zBy5EjMmjULAwcOxL///osZM2bA0NAQ48ePl+y3efNmzJ8/H7NmzcL27dsxceJEfPnll+jXrx/mz5+PiIgIjBs3Djdv3oSuri6EQiEsLCzw66+/wtjYGP/++y++/fZbNGzYUGFnp+TkZJiZmeHw4cN4+vQpRo4cCUdHRwQHB9f62smnTYuGzLGGnnqWdevWDR4eHvj+++/lrt+2bRvMzMywdu1aODs7o3v37li0aBF++eUXqZKGjY0Nli1bhmbNmsHZ2VmyfPDgwejfvz8cHBwQHByMV69ewdfXFwEBAXB0dMS0adNw+/ZtSZugpaUlpk2bBnd3d9ja2mLkyJHo1asXDh48qJLrdXFxAQCkpaUBALZs2YIOHTpg3rx5cHR0xMCBAzFlyhSsW7dOar8uXbpgzJgxcHBwQGhoKIqKimBnZ4chQ4bA3t4eISEheP36Ne7cuQOgbBKB7777Dq1bt0bTpk3Rt29fjB49uspqU0NDQ0RERMDZ2Rm+vr4IDAzEuXPnVHLt5NNGJTr2UKL7CCxduhSHDx/GlStXZNbdvXsXbdu2BZf74aXy8vJCcXExHj58KFnWsmVLucd2c3OT/N/U1LTSZZmZmQDKqvzWrFmD9u3bw87ODlZWVjh27BgyMjJqcYUfiEub4kHVqamp8PDwkNrGy8sLz549k5pdpHzMBgYG0NPTU3gdALB9+3Z06tQJDg4OsLKywqZNm6q8DmdnZ6l5Ks3NzaWOSUhNyWuiE4pYGcb8yaFE9xFo3bo1evfujUWLFsmsEyn4IJSfgUNfX1/uNtra2jLbl/8iFy8Tt11t2LABP/30E6ZNm4YjR44gMTERAQEBKuvgcvfuXQBlU74BZddX2Uwi5ZeXvw7xOkXXER0djdDQUAwdOhRG8pR/AAAgAElEQVSHDh1CYmIigoKCqrwOeedR9BoQUl3y+pMXCui9pQ4aO8V6TdrM2LRw4UJ4eHjgr7/+klru4uKC6OhoCIVCSakuOTkZOjo6ctv0ais5ORndu3fH4MGDAZQlovv376tswuwNGzbAyMgInTp1AgA0a9YMKSkpMjFYWVnVamLk5ORktGnTBuPGjZMse/ToUY2PR0htCeXktCIBoKex38IfDyrRfSTs7e0xcuRI/Pzzz1LLg4KC8OLFC8ycORN3797FqVOnsGTJEowdO5aRiawdHR2RkJCA5ORk3Lt3DyEhIXj8+HGNjpWdnY2XL18iLS0NcXFxGDx4MI4cOYIffvhBkjgnTJiA8+fPIzw8HPfv38f+/fuxceNGTJs2rdbXcf36dcTFxeHBgwdYtWqVpMMNIWwQyKkZoBKdetBviY/I7NmzsWfPHqlllpaW2L17N8LCwtCxY0c0aNAA/fv3x8KFCxmJISQkBOnp6RgwYAB0dXUxdOhQDBgwAP/995/Sx/r6668BAPXr14elpSW8vLxw5swZNG/eXLKNu7s7fv31V6xYsQIREREwNTXF9OnTpUpiNTFq1CjcuHEDY8aMgUgkQu/evTF58uQaD5UgpLbkl+go0akDa5M6q9KTJ09gbW3NdhiM0dT5IAHNvbbc3Fy8evVKY+cU1PT5Epm4vl/vFmD6hRypZSl9TeHC165kD2Zo+msnD1VdEkKIGsituiyt8+WMOoESHSGEqAFVXbKHEh0hhKiBvJxGnVHUgxIdIYSogfxEp/44PkWU6AghRA3kzYJCJTr1oERHCCFqQG107KFERwghaiAv0VGJTj0o0RFCiBrIy2lUolMPSnSEEKIGNI6OPZToNID4zthiQqEQ06dPh52dHfh8PhITE1mMrm6p+FwSoiryqi4pz6kHzXXJkoCAALi6umL16tVSy6OiojB79mw8fVr9O6SvWLFC6lYysbGxiIqKwvHjx2Fra4uGDRuqLO6aSkxMRK9evQCU3frGwMAA1tbWaN++PaZMmQJbW1t2A/x/FZ9LQlRFXi0l1VyqByU6DVDxFjoPHz6EmZmZzA1NlVVSUiJzf7baSklJQcOGDVFQUIAbN25g06ZN6NChA/bt2wdvb2+VnqsmVHU7IkIqkje8QF51JlE9qrr8yE2cOBHDhg3D5s2b8dlnn6Fp06aYNGkS3r17J7WNuLpt4sSJmDdvHjIyMsDn8yV3CigqKsLcuXPh5OQEMzMzdO3aFcnJyZJjJCYmgs/nIzY2Fr6+vjAxMcFff/2F8PBweHl5Yffu3WjevDmsrKwwadIkFBcXIzIyEm5ubrCzs8O8efMkNz1VxMTEBGZmZrC3t0efPn0QHR0Nd3d3TJkyBQLBh9GzO3bsQKtWrWBiYoJWrVph586dUsfh8/nYtm0bhgwZAgsLC7Rp0wYJCQl4+vQp+vXrB0tLS3h7e+Pq1auSfbKzsxEUFARXV1eYm5vD09NT5m4GFasuAwICMHPmTCxduhT29vZwdHTE/Pnzq3WthJQnr+pS3jKiehpbojMY0Umt58vfeZaxY1+8eBGWlpY4fPgwnj59ipEjR8LR0RHBwcEy265YsQLW1taIiorC6dOnwePxAJTd2PXw4cP46aefYGtri40bN6J///64fPkyzM3NJfsvXrwYYWFhsLe3h4GBAa5cuYLHjx/jxIkT2LdvH54/f47hw4fj1atXMDU1RXR0NO7du4dRo0bBw8MDffr0UeraeDweJk6ciOHDh+P69eto1aoVjh07hpCQECxfvhy+vr7466+/MHPmTJiamsLf31+y75o1a/D9998jLCwM4eHhGDNmDD7//HMEBQVh1apVCA0NxaRJkyT3oSssLESLFi3w7bffwsjICGfPnsWMGTNgbW0NHx+fSmM8cOAAxo8fj9jYWMmtf1q2bIn+/fsrda3k00ZVl+yhEl0dYGhoiIiICDg7O8PX1xeBgYE4d+6c3G0bNGgAQ0NDcLlcmJmZoXHjxigoKMD27duxePFi+Pn5wdnZGT/88ANMTEwQGRkptf+cOXPg6+sLW1tbNG7cGAAgEAiwceNGuLq6okuXLujSpQuuXLmCH3/8Ec7OzujVqxc8PDyQlJRUo+tzcXEBAKSlpQEAfvrpJwwaNAjjxo2Do6Mjxo8fjwEDBmDdunVS+w0ePBj9+/eHg4MDgoOD8erVK/j6+iIgIACOjo6YNm0abt++jaysLABl9/abNm0a3N3dYWtri5EjR6JXr144ePCgwvicnZ3x3XffwdHREX379kXHjh0rff4JqYy80hu1B6sHJbo6oFmzZtDS+lD4Njc3R2ZmZrX3f/ToEUpKSuDp6SlZxuPx0K5dO5kbqrZq1Upm/yZNmki1XZmamsLR0RE6OjpSy5SJqTzxh53D4QAA7t69K9O+6OXlJROrm5ub1PkrWyaOSyAQYM2aNWjfvj3s7OxgZWWFY8eOISMjQ2F85Y8JKP/8EwLIb4+jEp16aGzV5cfO0NAQubm5Mstzc3NhZGQktax8kgPKEoIyvwQrJpKKxypPX19fZpuKHVI4HI7cmMq3sSlDnMDK97ysTqzl4xKvKx+XeJm4PW3Dhg346aefsGLFCri6usLAwABLly6tMmnJu376JU6URVWX7NHYRMdkm5kqODk5IS4uDiKRSOoL/Nq1a3B0dFTpuezt7aGjo4Pk5GRJMhEIBLh06RLr7UwCgQA///wz7OzsJB1nnJ2dkZKSgv/973+S7ZKTkyVVnDWVnJyM7t27Y/DgwQDKfgDcv3+feloStZD324g6o6iHxia6j11QUBB++eUXzJ49G8OHD4euri5iY2Nx6NAh7N69W6Xn0tfXx+jRo7FkyRIYGxujadOm2LRpEzIzMzFmzBiVnqsqmZmZKC0tRUFBAW7evImNGzfi5s2b2L9/v6TjzNSpUzFy5Ei0bNkSvr6+iI+Px4EDB/Dbb7/V6tyOjo74448/kJycDGNjY2zduhWPHz+WJFhCmCS/6pIynTpQomOJra0tTpw4gbCwMPTr1w9FRUVwcnLCr7/+im7duqn8fEuWLAEATJ48Gbm5uXB3d8fBgwelelyqg7idsPyA8S1btkhVW/bs2ROrVq3Chg0bEBoaCmtra6xdu1aqx2VNhISEID09HQMGDICuri6GDh2KAQMGyLT9EcIEedWUVKJTD05OTk6df6qfPHkCa2trtsNgTGFhIXR1ddkOgxGaem25ubl49eoVnJyc2A6FEampqRp7bQAz1zcl6Q1+T30ntWyMiz7WePFVep6qaPprJw/1uiSEEDWQ3xmlzpcz6gRKdIQQogbykhpVXaoHJTpCCFEDeYU3Gl6gHpToCCFEDagzCnso0RFCiBrIS2rURqceGpHohEIhzVRBPhr0XiTyUBsdezQi0ZWUlEjdtoYQtohEIuTk5MidSo182qjqkj0aMWBc/Ata3tyRmiAvL09m/ktNoYnXZmhoKDMXKCHyqy7VH8enSGM+jZr8C/rVq1caOyBek6+NkPLk3WFc3jKiehpRdUkIIR87unsBeyjREUKIGlDVJXso0RFCiBrIS2rUQ1c9KNERQogayGuPoxKdelCiI4QQNaDhBeyhREcIIWpAbXTsoURHCCFqIL/qkjKdOrCa6F68eIEJEybAwcEBZmZm8PDwQFJSEpshEUIII6jqkj2sDRjPycmBn58fPD09sX//fhgbGyM9PR0mJiZshUQIIYyhRMce1hLd+vXrYW5uji1btkiW2drashUOIYQwSl5So0SnHqxVXcbExKBNmzYYNWoUHB0d4e3tja1bt9K4EkKIRpLXHkdtdOrBWqJLS0vDtm3bYGtri0OHDmHChAlYsmQJfvnlF7ZCIoQQxlCvS/ZwcnJyWHmqTUxM0KpVK8TGxkqWLV26FMePH8elS5cq3S81NVUd4RFCiEoNuKyLtPfSZQtnfSF+b1XIUkTsc3JyUst5WGujMzMzg7Ozs9SyZs2aISMjQ+F+8p6Y1NRUtT1hbNDk66Nrq5s0+doAZq6Pd/0F8F4gtUxLRwdOTuq9e4emv3bysFZ16enpifv370stu3//Pt2yhRCikeRVXVITnXqwlugmTZqEv//+G2vWrMHDhw9x+PBhbN26FWPGjGErJEIIYQzdpoc9rCW61q1bIyoqCn/88Qe8vLzw/fffY968eZToCCEaiTqjsIfVO4z7+fnBz8+PzRAIIUQt6A7j7KG5LgkhRA2o6pI9lOgIIUQN5E4Bpv4wPkmU6AghRA3kTgFGmU4tKNERQoga0BRg7KFERwghaiAvp1GBTj0o0RFCiBrI7YxCmU4tKNERQogayKumFIKqLtWBEh0hhKgBDRhnDyU6QghRA7nDC6jqUi0o0RFCiBrQODr2UKIjhBCGiSoZRkDDC9SDEh0hhDCssrY4ee12RPUo0RFCCMMqS3TUGUU9KNERQgjDKiu5CUWVV2sS1aFERwghDFPUFkdpjnlKJ7rY2FgIqU8sIYRUm6K2OKq+ZJ7SiW7QoEFwcXFBaGgorl69ykRMhBCiURQlOuqQwjylE93evXvRsWNH7Nq1C76+vvDw8MAPP/yAjIwMJuIjhJA6T1HVJQ0xYJ7Sic7Pzw/btm3D3bt3sWHDBlhYWCAsLAwtWrRAr169EBUVhbdv3zIRKyGE1ElUdcmuGndGMTAwwDfffIPDhw/j1q1bWLx4Md68eYOpU6fC2dkZY8aMwV9//aXKWAkhpE5SlMyo6pJ5Kul1WVJSguLiYhQXF0MkEsHQ0BDJycno378/2rdvj5s3b6riNIQQUidRomNXjRNdbm4udu7ciR49eqBly5ZYvXo1XF1dsXfvXty+fRs3b97Enj17UFBQgKlTp6oyZkIIqVOECtrhFK0jqqGl7A4xMTHYt28fYmNjUVRUhLZt22L16tXo168f+Hy+1Lbdu3fHq1evMHPmTJUFTAghdY2iEh210TFP6UQ3bNgwWFlZYfLkyRgyZAgcHR0Vbu/m5oYBAwbUOEBCCKnrFBXaKNExT+lE98cff8DHxwccDqda27dp0wZt2rRROjBCCNEUioYQUBsd85Ruoztw4AAuX75c6frLly9j8uTJtQqKEEI0ieKqS8p0TFM60e3evRuPHj2qdH16ejr27NlTq6AIIUST0Mwo7FL5pM7Z2dmoV6+eqg9LCCF1Fg0vYFe12ujOnz+PpKQkyeNjx47h4cOHMtvl5OQgOjoan3/+ueoiJISQOo6mAGNXtRJdYmIiVq5cCQDgcDg4duwYjh07JndbJycnhIeHqy5CQgip4xTlMirRMa9aiW7q1KkYPXo0RCIRXFxcsGbNGvTq1UtqGw6HAz09Pejr6zMSKCGE1FU0jo5d1Up0+vr6kgR27do1NG7cGHp6eowGRgghmoI6o7BL6XF0NjY2TMRBCCEai9ro2FVlouvZsye4XC6io6OhpaUlU2UpD4fDwdGjR1USICGE1HXU65JdVSY6kUgEoVAoeSwUCqucFUVEv1AIIUSCqi7ZVWWii4mJUfiYEEKIYoruUEBVl8xT+YBxQggh0qjqkl1KJ7o7d+7ItL8lJCSgX79+8PX1xcaNG1UWHCGEaAJFyYyGFzBP6V6XixcvBgD07t0bAJCRkYGhQ4eiXr16MDExwYIFC9CwYUMMHTpUpYESQkhdRePo2KV0ie769eto37695PH+/fshFAqRmJiIlJQU+Pn5ITIyUqVBEkJIXaaoHY467zFP6USXnZ0NY2NjyeO4uDh07NgRlpaWAAA/Pz/cv39fdRESQkgdR1WX7FI60ZmYmODx48cAyiZx/ueff9C5c2fJ+qKiItVFRwghGoA6o7BL6Ta6zp07Y+vWrTAyMpLc0aBHjx6S9f/99x+srKxUFyEhhNRximonqUTHPKUT3cKFC3H//n0sWLAA2traWLx4sWRasMLCQhw+fBgDBw5UeaCEEFJX0RRg7FI60ZmYmODPP/9EXl4edHV1oaOjI1knEolw9OhRNGnSRKVBEkJIXUZVl+yq8YBxIyMjqSQHAPXr10fz5s3RsGFDpY+3du1a8Pl8hISE1DQkQgj5KFFnFHYpXaIDAIFAgNOnTyMtLQ1v3ryR6R7L4XAwe/bsah/v77//xs6dO+Hm5laTcAgh5KOmeHiBGgP5RCmd6K5fv45hw4YhIyOj0vEfyiS63NxcjB07Fhs2bMCqVauUDYcQQj56igeMU6ZjmtJVl7NmzUJ+fj5+++03PHr0CG/evJH5y87Orvbxpk+fjj59+sDHx0fZUAghpE5QlMuojY55NSrRhYaGIiAgoNYn37lzJx4+fIgtW7ZUe5/U1FSllmsKTb4+ura6SZOvDVDt9T1/qQVAR+66Zy9eIFUoUNm5quNjee2cnJzUch6lE52pqSm0tGrUtCclNTUVS5cuxZ9//inTqUUReU9Mamqq2p4wNmjy9dG11U2afG2A6q+vcWk+8CBX/jpTMzg56avsXFXR9NdOHqWrLseNG4e9e/eipKSkVie+dOkSsrKy4OXlBWNjYxgbG+P8+fOIjIyEsbExzbBCCNEYNLyAXUoXzSwtLaGlpQUvLy8MGzYMTZo0AY/Hk9mub9++Co8TEBCAVq1aSS2bPHkyHBwcEBwcrFQpjxBCPmaU6NildKILCgqS/H/JkiVyt+FwOFUmOj6fDz6fL7VMT08PDRs2hKurq7JhEULIR0vRHcYp0TFP6UR37NgxJuIghBCNpXjAOGU6pimd6Ly9vZmIAwAQExPD2LEJIYQtdONVdtW4++T79+9x5coVZGZmokOHDmjcuLEq4yKEEI2hqERHVZfMq9Fclz///DOcnZ3Rs2dPjBo1Crdu3QIAZGVlwcbGBrt27VJpkIQQUpfR3QvYpXSii4qKQmhoKLp27YoNGzZITQNmbGyMzp07448//lBpkIQQUpcpqp6kPMc8pRPdxo0b4efnh+3bt8Pf319mfcuWLXH37l2VBEcIIZqA7l7ALqUT3YMHD+Dn51fpemNjY2RlZdUqKEII0SSKhhdQomOe0onO0NAQubnyp7IByhIhdUwhhJAPFHdGoUzHNKUT3ZdffomoqCi5U3Q9ffoUO3fuRNeuXVUSHCGEaAIaXsAupRPd/Pnz8fr1a3Tq1Am//PILOBwO4uLisHjxYnTo0AHa2tpK3XSVEEI0ncIpwNQXxidL6URnb2+PkydPwtzcHCtXroRIJMLGjRuxbt06tGjRAidPnoSVlRUTsRJCSJ2kcAowynSMq9GAcWdnZ/zxxx/IycnBw4cPIRQKYWtrS21zhBAiB91hnF1KJbqioiLs27cPZ86cwaNHj5Cfnw8DAwPY29vD19cXAwcOpLsOEEJIBTQzCruqnehu3bqFoUOH4smTJxCJRDAyMoKBgQEyMzNx7do1HD58GBEREdizZw+cnZ2ZjJkQQuoUGkfHrmq10eXn52PIkCHIzMzEggULcOvWLaSnp0v9O3/+fLx48QKDBw9GQUEB03ETQkidoah6UgjKdEyrVqKLiopCRkYG9u3bhxkzZsDS0lJqvaWlJYKDg7Fnzx6kp6dj9+7djARLCCF1kcISHXVGYVy1El1sbCx8fX3RsWNHhdv5+Pigc+fOOHnypEqCI4QQTUB3GGdXtRLd7du3q30fui+//BK3b9+uVVCEEKJJFFddEqZVK9G9efMGpqam1TqgiYkJ3rx5U6ugCCFEkygaQSCgIh3jqpXoioqKoK2tXa0Damlpobi4uFZBEUKIJqGZUdhV7eEFaWlpuHz5cpXbPXr0qFYBEUKIpqG5LtlV7UQXHh6O8PDwKrcTiUTgcDi1CooQQjSJwtv0UNUl46qV6DZu3Mh0HIQQorGo6pJd1Up0Q4cOZToOQgjRWDQFGLuUvnsBIYQQ5Si8ewElOsZRoiOEEIbR3QvYRYmOEEIYVkpTgLGKEh0hhDCsVEH9JOU55lGiI4QQhtE4OnZRoiOEEIYpKtGJqI2OcZToCCGEYQrb6CjPMY4SHSGEMExRhxNKdMyjREcIIQwrpXF0rKJERwghDFNcoqNMxzRKdIQQwjAq0bGLEh0hhDCsVEGJjhId8yjREUIIw2gKMHZRoiOEEIYpnBmF8hzjKNERQgjDaBwduyjREUIIw6hExy5KdIQQwjBqo2MXJTpCCGEY9bpkFyU6QghhGI2jYxclOkIIYRhVXbKLEh0hhDBIKBIpLLVRiY55lOgIIYRBVQ0foOEFzGMt0UVERKBz586wtraGg4MDBg0ahNu3b7MVDiGEMEJRRxSASnTqwFqiS0pKQlBQEE6dOoWjR49CS0sLgYGBePPmDVshEUKIyinqiAKUVW0SZmmxdeLo6Gipx1u2bIGNjQ1SUlLg7+/PUlSEEKJaim7RA1DVpTp8NG10+fn5EAqF4PP5bIdCCCEqU1WJrqqqTVJ7H02imzt3Lpo3b4527dqxHQohhKhMxUSmU+Fbt4SqLhnHycnJYf1ZnjdvHqKjo3Hy5EnY2toq3DY1NVU9QRFCiAq8KOKg19/1JY8NeCLkCziSx7pcERLbv2cjNNY5OTmp5TystdGJhYaGIjo6GseOHasyyQHyn5jU1FS1PWFs0OTro2urmzT52gDVXp/221Lg75eSxwY6POS//1DMKxVx1PpcavprJw+riW7OnDmIjo7G8ePH0axZMzZDIYQQRlTsjKKrxZF6XCoCRCIROBzp5UR1WEt0s2bNwr59+/D777+Dz+fj5cuyXzz6+vowMDBgKyxCCFGpip1RtLkcaHGk71FXIgR0eGoO7BPCWmeUyMhIvH37Fn369IGzs7Pkb8OGDWyFRAghKldx+IAWpyzZlVdCo8YZxVqJLicnh61TE0KI2lS86SqPy4E2D3gv+LCshIYYMOqjGV5ACCGaSG6JjkMlOnWiREcIIQyqOI5OiyvbHldMJTpGUaIjhBAGVeyMosXhQIva6NSKEh0hhDCoYomOx5UzOwolOkZRoiOEEAZVvIO4FocDnQolumIBCIMo0RFCCINkSnQcUNWlmlGiI4QQBsm00cmtulRjQJ8gSnSEEMIg2RIdhwaMqxklOkIIYZDMODouoE2dUdSKEh0hhDCo4swoWnJLdOqM6NNDiY4QQhgkt0THq9Drkkp0jKJERwghDJKZ65IDaFe4Iw+V6JjF+o1XCSFEk5XKlOg4MlOAURsds6hERwghDKp441UtTln1ZXlUomMWlegIIYRBsuPoONCpUICjEh2zqERHCCEMqnhnAh5HdnhBccUeK0SlqERHCCEMKqzQSFdfiwNRqfQ2VHXJLEp0hBDCoPcC2URXMbFR1SWzqOqSEEIY9L5CiU6Px6G5LtWMEh0hhDCoYqKrryV741UaMM4sSnSEEMKgdxVmddbVki3RVRxUTlSLEh0hhDCosMJNVfV4HOjITAGmxoA+QZToCCGEQe8rlOjkVV1SZxRmUaIjhBAGvavYGUVO1SV1RmEWJTpCCGFQxeEFujy68aq6UaIjhBAGyet1WXFmlCKaGYVRlOgIIYRBMuPotDjQrzCrc0EJJTomUaIjhBAGyc6MwoWRjnTV5VtKdIyiREcIIQyS1xnFsELd5VvqjcIoSnSEEMKgQjmdUQypRKdWlOgIIYQhAqEIRRUGjOvyIFOiy6MR44yiREcIIQyp2D6np8UBh8OhNjo1o0RHCCEMkRla8P9Tf+lrcVA+1b0rFdF8lwyiREcIIQyRdy86AOByODDUli7V5VOpjjGU6AghhCF5xbJVl2Iy7XTU85IxdIdxQghhgkiE7Kw3aPX2EayKssEvfYfWeUXQjtEBeDxMfFKA+8U6eKXTAK+0jVCQUx8waMR21BqJEh0hhNRWcRG46angPfoP3CcPwX36CNxnj9HjfQF6VNz237J/5spZLmzQCEIr2w9/TewhbOoE6NRj/ho0GCU6QghRhkgEzssM8O5eB+/hf+A+vANuxkNwhLWveuTmZoObmw3c/vfD6XhaENo4QuDoCqGDKwSObhA1Ngc4HAVHIuVRoiOEEEVEInBePAHvv2vg/XcVvP+ugpuTpbbTcwSl4D36D7xH/wFx0QAAYSMTCFxaSv5EppaU+BSgREcIIeWJRKiX9QJaGXfAu/P/iS03u0aHKtSqh4c6xnhSrzFeaxuijQ0f9o31AZEIKc/e4fGrHJgV58Ky6A2cC19CS1hareNyszPBvRAH7QtxAABhI1MIXFtB4O6J0s/bAvqGNYpXU1GiI4R82oRCcJ+mgXf3Grj3roP331W45r5R/jAmlhDYu0Bo5wxhEzsIrWzR9bwIf7/+kLxi/BujiXlZe9t/qQWYlJQjWdfTSgtRnxeC+zQN3Gfp4GY8Kmvzy3xe5bm52a/ATToF7aRTEHG5ELi0ROkXPhC0/RIio4ZKX4umoURHCPm0lJaCm3YXvHs3wLt7DbzUm+AUvFXqECIdXQicPoegWXMI7V0gsHcBDBpIbSMQinD/rXSSMq/Pk/zftaG21LobuSKILJtCYNkU5WcN4+RkgfvwDnj3b4P34Ba4D+6AU1JcaWwcoRBat/+F1u1/Idq1DgL3dijxCYCghReg9Wl+5X+aV00I+TSIROC8egZe2j1w0+6B++g/8B7cAae4ULnD6OhC0Kx5WZvYZy0htG0GaGkr3OdKVgneFH0YR9dAh4Omhh8SnTNfG1wOIJ4QJT1fgPS3pWhqKP21LOIbQ9DaG4LW3mULSorBffifpL2Ql3qz0sTHEQmhdS0FWtdSIGzQCKWde0Gr6edKXbsmoERHCNEMBW/LqvzEVX9PHoCXnqp0aQ0ABNr1IHJxl3T2ENo6K1UaKhaIMPdijtQyX0tdaHE/dBipr8WBeyNtXM0qkSz76VY+VnvyFR9cWwdCZ3cInd1R0mc4UFwE3oPb4F2/CN7VFPCepcndjZubDZ3DO+HG5UHg0RklX/WD0MG12tdUl1GiI4TUDSIRkJ8LbuYLcDOfgfPqObivnoKT+Rzc50/AzXld80PrGZSV2Jq5Q+DsjteoKlUAABFkSURBVLulXDi5fKZwnxKhCCkvi2FryAOXw4EWp+y+cvklIoxNeIPUXOmOJV81kR0LN/YzfUwu1073y50CnH5aCD9rXVjU54HL5SCvWIjP+NroaKEDA20udLiAQIQPSVOnHgSftYLgs1bAoAngvHgCrb/PQSvlNHgZD2XOyRUKwE2Oh3ZyPAR2Lij5qh9K23UCtHWUf+LqCNYTXWRkJNavX4+XL1/CxcUF4eHhaN++PdthEUKYJhQC7wvAyc8Dp+Dt///lAQVvy5bl54LzJgvcnNfgvHkNTk4WOKUlVR+3OqfmG0Pg7A5hM3cImrlD2MQOhUIOXr4XwFKfBzy4L7NPkUAEDoC4jEJEP3qPQ4/eV/t8doY89LXTk1k+wF4Py/99i6fvPrTKPcgTYNOtgiqP2cNGF5u8G4Jfj4t3pULU55XdGUFkbo2SXsNQ0vMbcNPuQftcDLSS48EpfCdzDN6j/8DbuhzCvZtR6hOA0i98ILRx1LihCpycnBzWZhKNjo7GuHHjsHbtWnh6eiIyMhK7d+9GSkoKrK2tq32c1NRUODk5MRgpu+rs9YlE8v+PD/9PTb0PJ0cHeasqPlBwPAXnRYV9RP+/TCT6sJ1IVG4ZwJG3XrJNJfvLWZ+eloamTZuWtROVX1/+fPL2r+Z6TnX2Lx9vNddLYhUKAUEpUFoKCAXgCARljwWlyHr5Eo35fEAo+LCNoLTcNgKgtAScovdAcRE4RYVAcaH0v0WFH66BQaL6+njXxBGFNs2g38wFLyyaIUVkjM+NdWCsy0V2oRAnHhci9FKuZB83AwGKuPWQVyKEhR4PqbmlMncKr66G9TiI7tYYrRrLLzFtupWPeeXOXVN6Whx8YaIDHS4Q/7QIbo200dWqHp6/E+D20xzMyU9G4L0/ofsqQ+FxhI3NIHBtA4GtM4S2ThA1NofIkA9w6+7UyKwmui5dusDNzQ3r16+XLGvdujX69OmDRYsWVfs41UkEvDtXoB2zB2Uf4PIf9LI/TiXLy/6EZetEgv//V1g2C4JknfDDv8IPyzgiYbW+7GUeVthOKBSCK36TSR2vsgMoOFfFV1tRUqj08B8eqOOLipDqKOFq4amhBZ7wmyC9gQ2eNLDCDSNbxBY3Qk5JWQnFSIeD96UiqGv+ZBsDHo52bwxbw8orzwRCEWYm5+Dgw/fIr2EyrS6OSIhu2Tcw+WksemRfrfZ+Ii1tiAyMAO16EOnolE1Jpl0PxT2/gaCFB4MRqwZria64uBgWFhbYtm0bAgMDJctnzZqF27dv48SJE2yERQghRMOwVhbNysqCQCCAiYmJ1HITExO8evWKpagIIYRoGtYrXTkVGj1FIpHMMkIIIaSmWEt0xsbG4PF4MqW3169fy5TyCCGEkJpiLdHp6OigZcuWOHPmjNTyM2fOwMPj42/cJIQQUjewOo5u8uTJGD9+PNq0aQMPDw9s374dL168wKhRo9gMixBCiAZhtY2uX79+CA8Px+rVq9GxY0ekpKRg//79sLGxqdVxL1++jMDAQFhZWaFJkybo1q0bsrLUd/8opolEInz99dfg8/k4cuQI2+HU2ps3bxASEoIvvvgC5ubmcHNzQ3BwMLKza3ZrlI9BZGQk3N3dYWZmBh8fH1y4cIHtkGotIiICnTt3hrW1NRwcHDBo0CDcvn2b7bAYsXbtWvD5fISEhLAdisq8ePECEyZMgIODA8zMzODh4YGkpCS2w1IL1jujjBkzBjdu3MCrV69w7tw5dOjQoVbH++eff9C3b194e3sjLi4OZ8+exZQpU6ClQbN2//TTT+DxeFVvWEc8f/4cz58/x5IlS3DhwgVs2bIFFy5cQFBQENuh1Uh0dDTmzp2LmTNnIiEhAe3atcOAAQPw5MkTtkOrlaSkJAQFBeHUqVM4evQotLS0EBgYiDdvlL+lzcfs77//xs6dO+Hm5sZ2KCqTk5MDPz8/iEQi7N+/HxcvXsSqVas+mf4QrA4YZ0K3bt3QsWNHLFiwgO1QGHHlyhUMGzYMZ8+ehZOTE3bu3Ik+ffqwHZbKxcbGYtCgQUhPT4eRkRHb4ShFVRMhfOzy8/NhY2ODqKgo+Pv7sx2OSuTm5sLHxwfr1q3DqlWr4OrqitWrV7MdVq0tXboU58+fx6lTp9gOhRWsl+hUKTMzE5cuXYKZmRm6d+8OJycn+Pv749y5c2yHphJv375FUFAQfvjhB43/Jfb27VvUq1cPenqy8wN+zIqLi3H16lX4+vpKLff19cXFixdZiooZ+fn5EAqF4POrmG2/Dpk+fTr69OkDHx8ftkNRqZiYGLRp0wajRo2Co6MjvL29sXXrVog+kZmNNCrRpaWlAQDCw8PxzTff4ODBg/Dy8kK/fv1w48YNdoNTgeDgYHTp0gXdunVjOxRG5eTkYNmyZRg+fHidq3L+lCZCmDt3Lpo3b4527dqxHYpK7Ny5Ew8fPsR3333Hdigql5aWhm3btsHW1haHDh3ChAkTsGTJEvzyyy9sh6YWdeJbJCwsDGvWrFG4zbFjx6CjUzZp6qhRo/C///0PANCiRQskJSVhx44diIj4v/buPybq+g/g+JNbpZLZB5yBcOVtBIidNahpuLDcmEtu4TzFw7UKN+dCZDnmIC8wd5TLUCmdrZDpMnVxIl4KSkXLLDLPxpLZ+nGcba1zqUMuCQLyru8fjvt6nuCJwHF3r8d2f9z7/eFzrzewz+vzft/7835vHfFYb5e/bXM4HJw9e9bncYyxzN+2ZWRkeN53dXWxbNkypk6dislkGukQR0yoL4RgNBr57rvvaGxsDInvi202GyaTiWPHjnmuI6HE7XaTmprqGTp/7LHHOHfuHNXV1axcuTLA0Y28oEh0+fn5LF26dNBj1Gq15445OTnZqy4pKYk//hh8xe5A8bdt+/fv5+effyY+Pt6rbvny5cyaNYvGxsaRDHNI/G1bv7///pucnBwAampqGD9+/IjGNxLCYSGEdevWUVdXx5EjR9BoNIEOZ1hYrVba29tJT0/3lLlcLr799lt27drF+fPnGTfOdz+5YBETExNU18XhFhSJbvLkyUyePPmWx02bNo2pU6dis9m8yu12OzNmjM2ddP1tW1lZGYWFhV5lc+bMoby8HJ1ON1Lh3RF/2wbXvpPLycnhv//+o7a2lokTJ45wdCPj+oUQrl+s/MsvvyQ7OzuAkQ2PkpIS6urqqK+vJykpKdDhDBudTkdqaqpXWUFBAQkJCRQVFQV9L+/JJ5+krc17j722trbb2g4tmAVFovNXREQEhYWFvPXWW2i1Wh599FEOHTrE6dOnefvttwMd3h2Ji4sjLi7Op1ytVgf9XXVnZyd6vZ7Ozk727dtHd3c33d3XNomMiooKuotMqC6EsHbtWmpqati7dy+KonDhwgUA7r333qC9MemnKIrPpJrIyEiioqLG7E3y7Vi1ahXz589n8+bN6PV6WltbqaqqCtnZ6TcKqUQH1/6g//77L6WlpVy+fJnp06dTW1vLzJkzAx2aGMAPP/zA6dOnAXj88ce96m78Di8Y6PV6Ll++TEVFBRcuXCAlJWVYFkIItOrqagCfx1lKSkpYt25dIEISfkpLS2Pfvn2YTCYqKipQq9UYjUZWrFgR6NBGRcg9RyeEEEJcL6QeLxBCCCFuJIlOCCFESJNEJ4QQIqRJohNCCBHSJNEJIYQIaZLohBBChDRJdEIMg5UrV/qsrDEW2e12Fi9ezEMPPYSiKNTU1AQ6JCFGnCQ6ETZyc3OJiYnB6XQOeIzRaERRFH788cdRjGz0rF69mjNnzmA0Gvnggw+YPXt2oEMSYsRJohNhw2Aw0Nvby+HDh29a73a7qaur45FHHgmp3aX79fX1cerUKXJzc3n55ZcxGAxBv3ycEP6QRCfCxoIFC5g0aRIHDhy4af2JEyf4888/MRgMoxzZ6Ghvb8ftdnP//fcHOhQhRpUkOhE2xo8fT3Z2Ns3NzZw/f96n3mw2o1KpWLJkiadsz549PPfccyQmJvLAAw/wxBNPsG3btlvuzHzu3LkBvwObMWOGz04UTqcTo9GIVqtlypQpaLVaTCYTfX19frWtubkZnU5HXFwcarWaRYsW0dLS4ql/4403SElJAeDNN99EUZRBd5b48MMPURSFPXv2eJVXVVWhKApms9mvuIQYC0JuUWchBrN06VL27t3LwYMHvZJNT08P9fX1PPXUU167ROzcuZOUlBTmz5/PhAkT+OKLL1i/fj2dnZ3DthN1d3c3Op0Oh8NBXl4eGo2G1tZW3n33Xdra2nySzY1OnDjB4sWLUavVFBcXc/XqVXbv3o1Op+Po0aOkpqaycOFCpkyZQklJCQsXLiQrKwuVauD73JdeeomGhgZee+01nn76aaZNm4bdbmfDhg1kZ2ffcp9BIcYSSXQirGRkZKBWqzlw4IBXomtsbOTKlSs+F/BPP/2UyMhIz/sVK1aQn5/P+++/T3FxMXffffcdx7R9+3Z+++03vvrqKxITEz3lSUlJvPrqq1itVmbNmjXgzxuNRiZNmkRTU5Onl2YwGJg9ezalpaU0NDQwc+ZMoqOjKSkpQavV+jU8u337dtLT0ykoKMBisZCfn8/EiROprKy84zYLMZpk6FKElYiICJYsWUJrayu//PKLp9xsNnuGNq/Xn+RcLhdOp5P29nYyMjLo7OzEbrcPS0wWi4U5c+YQHR1Ne3u75zVv3jzgWo9tIA6Hg7Nnz/L88897DUU++OCD6PV6Tp48yZUrV4YUV0xMDFu2bOGbb77h2WefxWq18s477/i9ma4QY4UkOhF2+nsz/ZNSnE4nTU1Nnskq12tubmbBggXExsai0WhISEhg1apVAPz111/DEo/dbqepqYmEhASvV//U/0uXLg34s7///juAV0+wX3JyMm63G4fDMeTYFi1aRFZWFt9//z0Gg4GsrKwhn0uIQJGhSxF2UlJS0Gq11NbWUlpaisVioa+vz2fY0m63o9frSUxMZNOmTcTHxzNu3DhaWlowmUy43e4BPyMiImLAOpfL5fXe7XYzb948XnnllZseHx8ffxutG14dHR2eSS02m42rV69y111y2RDBRf5jRVgyGAyUlZVhtVoxm81ER0eTmZnpdczRo0fp7e3FbDZ7TVDxZ8hSURTAt9f3zz//cPHiRa8yjUZDV1cXzzzzzG23o3/XcpvN5lP366+/olKp7ihRrl27lo6ODsrLyykrK2Pr1q0UFxcP+XxCBIIMXYqwlJOTg0qlorKykpMnT6LX630mlvTPSrz+UYKenh6qq6tvef6oqCgUReHrr7/2Kq+urvZ5NEGv12O1Wvnss898ztPd3U1XV9eAnxMfH49Wq2X//v10dHR4yh0OB3V1daSnp/sMx/rrk08+4eDBg5SWllJYWMgLL7xARUUFZ86cGdL5hAgU6dGJsBQbG8vcuXM5duwYwE2ny2dmZrJhwwZycnLIy8ujp6eHjz/+2O+hu+XLl1NZWcnq1atJS0ujpaWF5uZmoqKivI5bs2YNn3/+OcuWLSM3N5fU1FR6e3ux2WwcOnQIi8Uy6DqaGzduRK/Xk5mZyYsvvojL5WLXrl24XC7Ky8tv47fyfxcvXqSoqMgz67L/c44fP05+fj7Hjx/nnnvuGdK5hRht0qMTYat/UopGo7np9P3k5GQ++ugjVCoV69evp6qqCp1Ox+uvv+7X+YuLi8nLy+PIkSOUlZVx6dIlLBYLEyZM8DouMjKS+vp6ioqKOHXqFEajkc2bN9Pa2kpBQQEJCQmDfs7cuXOxWCzExsayadMmtmzZwsMPP0xDQwNpaWl+/ja8rVmzhp6eHt577z1Pz/a+++5jx44d/PTTT2zcuHFI5xUiECKcTufgSzwIIYQQQUx6dEIIIUKaJDohhBAhTRKdEEKIkCaJTgghREiTRCeEECKkSaITQggR0iTRCSGECGmS6IQQQoQ0SXRCCCFCmiQ6IYQQIe1/QkM2ghrM0kUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18536e1a2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(results_df['x'], label = 'Normal Domain')\n",
    "sns.kdeplot(tpe_results['x'], label = 'Uniform Domain')\n",
    "plt.legend(); plt.xlabel('Value of x'); plt.ylabel('Density'); plt.title('Comparison of Domain Choice using TPE');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest Value of the Objective Function = -219.8012 at x = 4.8782 found in 956 iterations.\n"
     ]
    }
   ],
   "source": [
    "print('Lowest Value of the Objective Function = {:.4f} at x = {:.4f} found in {:.0f} iterations.'.format(results_df['loss'].min(),\n",
    "                                                                             results_df.loc[results_df['loss'].idxmin()]['x'],\n",
    "                                                                             results_df.loc[results_df['loss'].idxmin()]['iteration']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Well that definitely shows the value of choosing a good prior! When it comes to hyperparameter tuning, we often do not know ahead of time what values to concentrate around but we can try to use past experience or the work of others to inform our search spaces. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One-Line Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x': 4.878181920398149}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just because you can do it in one line doesn't mean you should! \n",
    "best = fmin(fn = lambda x: np.poly1d([1, -2, -28, 28, 12, -26, 100])(x) * 0.05,\n",
    "            space = hp.normal('x', 4.9, 0.5), algo=tpe.suggest, \n",
    "            max_evals = 2000)\n",
    "\n",
    "best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Conclusions\n",
    "\n",
    "In this notebook, we saw a basic implementation of Bayesian Model-Based optimization using Hyperopt. This requires four parts:\n",
    "\n",
    "1. Objective: what we want to minimize\n",
    "2. Domain: values of the parameters over which to minimize the objective\n",
    "3. Hyperparameter optimization function: how the surrogate function is built and the next values are proposed\n",
    "4. Trials consisting of score, parameters pairs\n",
    "\n",
    "The differences between random search and and Sequential Model-Based Optimization is clear: random search is _uninformed_ and therefore requires more trials to minimize the objective function. The Tree Parzen Estimator, an algorithm used for SMBO, spends more time choosing the next values, but overall requires fewer evaluations of the objective function because it is able to _reason_ about the next values to evaluate. Over many iterations, SMBO algorithms concentrate the search around the most promising values, yielding:\n",
    "\n",
    "* Lower scores on the objective function\n",
    "* Faster optimization\n",
    "\n",
    "Bayesian model-based optimiziation means construction a probability model $p(y | x)$ of the objective function and updating this model as more information is collected. As the number of evaluations increases, the model (also called a surrogate function) becomes a more accurate respresentation of the objective function and the algorithm spends more time evaluating promising values.\n",
    "\n",
    "\n",
    "This notebook showed a the basic implementation of Bayesian Model-Based optimization using Hyperopt, but already we can see how it has significant advantages over random or grid search based methods. In future notebooks we will explore using Bayesian model-based optimization on more complex problems, namely hyperparameter optimization of machine learning models. "
   ]
  }
 ],
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